Abstract
The development of object detection has led to huge improvements in human interaction systems. Object detection is a challenging task because it involves many parameters including variations in poses, resolution, occlusion, and daytime versus nighttime detection. This study surveys on various aspects of object detection that includes (1) basics of object detection, (2) object detection techniques, (3) datasets, (4) metrics and deep learning libraries. This study presents a systematic analysis of recent publications on object detection covering around 400 research articles and synthesised the findings to provide empirical answers to research questions. The review is based on relevant articles published from 2015 through 2022, as well as discussions of challenges and future directions in this field. Furthermore, the survey examined the contributions of various researchers concerning their respective application domains, while emphasizing the advantages and disadvantages of the research work. Despite the success of various methods proposed in literature for predicting results, there remains room for improvement in the accuracy of object detection.
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Data availability
Datasets analysed during the current study are avaiable in the respective articles as shown in the table such as table number 4, 5, 9, 10, 11, 12, 13 and 14.
References
Ahmadi M, Ouarda W, Alimi AM (2020) Efficient and Fast Objects Detection Technique for Intelligent Video Surveillance Using Transfer Learning and Fine-Tuning. Arab J Sci Eng 45(3):1421–1433. https://doi.org/10.1007/s13369-019-03969-6
Alam A, Jaffery ZA (2020) Indian Traffic Sign Detection and Recognition. Int J Intell Transp Syst Res 18(1):98–112. https://doi.org/10.1007/s13177-019-00178-1
Alom MZ, Taha TM, Yakopcic C, et al 2018 The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
An N, Qi Yan W (2021) Multitarget Tracking Using Siamese Neural Networks. ACM Trans Multimed Comput Commun Appl 17(2s):1–16. https://doi.org/10.1145/3441656
Andrianov DE, Eremeev SV and Kuptsov KV 2015 The Review of Spatial Objects Recognition Models and Algorithms. Procedia Eng 129374–379. https://doi.org/10.1016/j.proeng.2015.12.126
Antioquia AMC, Tan DS, Azcarraga A, et al 2018 ZipNet: ZFNet-level Accuracy with 48× Fewer Parameters. In: VCIP 2018 - IEEE International Conference on Visual Communications and Image Processing. IEEE, 1–4
Arnold E, Al-Jarrah OY, Dianati M et al (2019) A Survey on 3D Object Detection Methods for Autonomous Driving Applications. IEEE Trans Intell Transp Syst 20(10):3782–3795. https://doi.org/10.1109/TITS.2019.2892405
Aslam A and Curry E 2021 A Survey on Object Detection for the Internet of Multimedia Things (IoMT) using Deep Learning and Event-based Middleware: Approaches, Challenges, and Future Directions. Image Vis Comput 106104095. https://doi.org/10.1016/j.imavis.2020.104095
Athanasiadis I, Mousouliotis P and Petrou L 2018 A framework of transfer learning in object detection for embedded systems. arXiv
Ayalew AM, Salau AO, Abeje BT and Enyew B 2022 Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients. Biomed Signal Process Control 74(October 2021): 1–11. https://doi.org/10.1016/j.bspc.2022.103530
Aziz L, Salam MSBH, Sheikh UU and Ayub S 2020 Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review. IEEE Access 8170461–170495. https://doi.org/10.1109/ACCESS.2020.3021508
Bach M, Stumper D, Dietmayer K (2018) Deep Convolutional Traffic Light Recognition for Automated Driving. In: In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, pp 851–858
Baek Y, Lee B, Han D et al (2019) Character Region Awareness for Text Detection. In: In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 9365–9374
Bamne B, Shrivastava N, Parashar L and Singh U 2020 Transfer learning-based Object Detection by using Convolutional Neural Networks. Proc Int Conf Electron Sustain Commun Syst ICESC 2020 (Icesc): 328–332. https://doi.org/10.1109/ICESC48915.2020.9156060
Banerjee K, Notz D, Windelen J, et al 2018 Online Camera LiDAR Fusion and Object Detection on Hybrid Data for Autonomous Driving. In: 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 1632–1638
Behrendt K, Novak L and Botros R 2017 A deep learning approach to traffic lights: Detection, tracking, and classification. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 1370–1377
Bergstrom T and Shi H 2020 Human-Object Interaction Detection: A Quick Survey and Examination of Methods Trevor. In: Proceedings of the 1st International Workshop on Human-centric Multimedia Analysis. ACM, New York, NY, USA, 63–71
Bhamare D, Suryawanshi P (2018) Review on Reliable Pattern Recognition with Machine Learning Techniques. Fuzzy Inf Eng 10(3):362–377. https://doi.org/10.1080/16168658.2019.1611030
Bochkovskiy A, Wang C and Liao HM 2020 YOLOv4: Optimal Speed and Accuracy of Object Detection
Borisyuk F, Gordo A and Sivakumar V 2018 Rosetta: Large scale system for text detection and recognition in images. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, New York, NY, USA, 71–79
Borji A, Cheng M-M, Hou Q et al (2019) Salient object detection: A survey. Comput Vis Media 5(2):117–150. https://doi.org/10.1007/s41095-019-0149-9
Boruah A, Kakoty NM, Ali T (2018) Object Recognition based on Surface Detection-A Review. Procedia Comput Sci 13363–74. https://doi.org/10.1016/j.procs.2018.07.009
Bouras C and Michos E 2022 An online real-time face recognition system for police purposes. In: 2022 International Conference on Information Networking (ICOIN). IEEE, 62–67
Braun M, Krebs S, Flohr F, Gavrila DM (2019) EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes. IEEE Trans Pattern Anal Mach Intell 41(8):1844–1861. https://doi.org/10.1109/TPAMI.2019.2897684
Burlina P, Pacheco KD, Joshi N, et al 2017 Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med 8280–86. https://doi.org/10.1016/j.compbiomed.2017.01.018
Caesar H, Bankiti V, Lang AH, et al 2020 nuScenes: A Multimodal Dataset for Autonomous Driving. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 11618–11628
Cao Z, Simon T, Wei S-E and Sheikh Y 2017 Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1302–1310
Cao Y, Peng H, Wu J, et al 2021 Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs. In: Proceedings of the Web Conference 2021. ACM, New York, NY, USA, 3383–3395
Ch’ng CK and Chan CS 2017 Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 935–942
Chahal KS and Dey K 2018 A Survey of Modern Object Detection Literature using Deep Learning. arXiv
Chapel MN and Bouwmans T 2020 Moving objects detection with a moving camera: A comprehensive review. Comput Sci Rev 38100310. https://doi.org/10.1016/j.cosrev.2020.100310
Chen Q, Wang P, Cheng A, et al. 2020 Robust one-stage object detection with location-aware classifiers. Pattern Recognit 105. https://doi.org/10.1016/j.patcog.2020.107334
Chen Y, Wang W, Zhou Y, et al 2021 Self-Training for Domain Adaptive Scene Text Detection. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 850–857
Chen Z, Ouyang W, Liu T, Tao D (2021) A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection. Int J Comput Vis 129(4):1121–1138. https://doi.org/10.1007/s11263-020-01412-0
Chen Z, Luo R, Li J et al (2021) U-Net Based Road Area Guidance for Crosswalks Detection from Remote Sensing Images. Can J Remote Sens 47(1):83–99. https://doi.org/10.1080/07038992.2021.1894915
Chen J, Bai S, Wan G, Li Y (2023) Research on YOLOv7-based defect detection method for automotive running lights. Syst Sci Control Eng 11(1). https://doi.org/10.1080/21642583.2023.2185916
Cheng G and Han J 2016 A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 11711–28. https://doi.org/10.1016/j.isprsjprs.2016.03.014
Cheng M, Su J, Li L and Zhou X 2020 A-DFPN: Adversarial Learning and Deformation Feature Pyramid Networks for Object Detection. 2020 IEEE 5th Int Conf Image, Vis Comput ICIVC 2020 11–18. https://doi.org/10.1109/ICIVC50857.2020.9177437
Chetouane A, Mabrouk S, Jemili I and Mosbah M 2020 Vision-based vehicle detection for road traffic congestion classification. Concurr Comput (July): 1–27. https://doi.org/10.1002/cpe.5983
Choi JD, Kim MY (2023) A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection. ICT Express 9(2):222–227. https://doi.org/10.1016/j.icte.2021.12.016
Choi J, Chang HJ, Yoo YJ, Choi JY (2012) Robust moving object detection against fast illumination change. Comput Vis Image Underst 116(2):179–193. https://doi.org/10.1016/j.cviu.2011.10.007
Cole JH, Poudel RPK, Tsagkrasoulis D, et al 2017 Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 163115–124. https://doi.org/10.1016/j.neuroimage.2017.07.059
Cordts M, Omran M, Ramos S, et al 2016 The Cityscapes Dataset for Semantic Urban Scene Understanding. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 3213–3223
Dai J, Li Y, He K and Sun J 2016 R-FCN: Object Detection via Region-based Fully Convolutional Networks. Adv Neural Inf Process Syst 379–387
Dai J, Qi H, Xiong Y, et al 2017 Deformable Convolutional Networks. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 764–773
Dalal N and Triggs B 2005 Histograms of Oriented Gradients for Human Detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). IEEE, 886–893
De Cesaro Júnior T and Rieder R 2020 Automatic identification of insects from digital images: A survey. Comput Electron Agric 178(September): 105784. https://doi.org/10.1016/j.compag.2020.105784
de Charette R and Nashashibi F 2009 Traffic light recognition using image processing compared to learning processes. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 333–338
Deng J, Dong W, Socher R, et al 2009 ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 248–255
Deng Z, Sun H, Zhou S et al (2018) Multi-scale object detection in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens 145(June):1–21. https://doi.org/10.1016/j.isprsjprs.2018.04.003
Dey B, Kundu MK (2019) Turning video into traffic data - An application to urban intersection analysis using transfer learning. IET Image Process 13(4):673–679. https://doi.org/10.1049/iet-ipr.2018.5985
Dhillon A, Verma GK (2020) Convolutional neural network: a review of models, methodologies and applications to object detection. Prog Artif Intell 9(2):85–112. https://doi.org/10.1007/s13748-019-00203-0
Ding P, Zhang Y, Deng W-J et al (2018) A light and faster regional convolutional neural network for object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 141(June 2017):208–218. https://doi.org/10.1016/j.isprsjprs.2018.05.005
Dollar P, Wojek C, Schiele B and Perona P 2009 Pedestrian detection: A benchmark. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 304–311
Dollar P, Wojek C, Schiele B and Perona P 2012 Pedestrian Detection: An Evaluation of the State of the Art. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 743–761
Dominguez-Sanchez A, Orts-Escolano S, Garcia-Rodriguez J and Cazorla M 2018 A New Dataset and Performance Evaluation of a Region-based CNN for Urban Object Detection. In: 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8
Du M (2018) Mobile payment recognition technology based on face detection algorithm. Concurr Comput 30(22):1–9. https://doi.org/10.1002/cpe.4655
Du F, Wang WL, Zhang Z (2020) Pedestrian detection based on a hybrid Gaussian model and support vector machine. Enterp Inf Syst 00(00):1–12. https://doi.org/10.1080/17517575.2020.1791363
Duan J, Xu Y, Kuang Z, et al 2019 Geometry Normalization Networks for Accurate Scene Text Detection. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 9137–9146
Elmahdy MS, Jagt T, Zinkstok RT et al (2019) Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer. Med Phys 46(8):3329–3343. https://doi.org/10.1002/mp.13620
Ertler C, Mislej J, Ollmann T et al (2019) The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. Comput Vis Pattern Recognit:1–17
Everingham M, Eslami SMA, Van Gool L et al (2015) The Pascal Visual Object Classes Challenge: A Retrospective. Int J Comput Vis 111(1):98–136. https://doi.org/10.1007/s11263-014-0733-5
Fan DP, Wang W, Cheng MM and Shen J 2019 Shifting more attention to video salient object detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 8546–8556
Fan D, Fang S, Liu X, et al 2019 A multi-scale face detection algorithm based on improved SSD model. In: Proceedings of the ACM Turing Celebration Conference - China. ACM, New York, NY, USA, 1–9
Fan D-P, Ji G-P, Zhou T, et al 2020 PraNet: Parallel Reverse Attention Network for Polyp Segmentation. In: The Legal Literature of Accounting. 263–273
Fan DP, Zhou T, Ji GP et al (2020) Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Trans Med Imaging 39(8):2626–2637. https://doi.org/10.1109/TMI.2020.2996645
Fang F, Li L, Gu Y et al (2020) A novel hybrid approach for crack detection. Pattern Recogn 107. https://doi.org/10.1016/j.patcog.2020.107474
Fernandes D, Silva A, Névoa R, et al 2021 Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy. Inf Fusion 68161–191. https://doi.org/10.1016/j.inffus.2020.11.002
Fregin A, Muller J, Krebel U and Dietmayer K 2018 The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 3376–3383
Fu C, Liu W, Ranga A, et al. 2017 DSSD: Deconvolutional Single Shot Detector
Fu K, Chang Z, Zhang Y et al (2020) Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images. ISPRS J Photogramm Remote Sens 161(January):294–308. https://doi.org/10.1016/j.isprsjprs.2020.01.025
Gao X, Li W, Loomes M and Wang L 2017 A fused deep learning architecture for viewpoint classification of echocardiography. Inf Fusion 36103–113. https://doi.org/10.1016/j.inffus.2016.11.007
Gawande U, Hajari K and Golhar Y 2022 SIRA: Scale illumination rotation affine invariant mask R-CNN for pedestrian detection. Appl Intell. https://doi.org/10.1007/s10489-021-03073-z
Gawande U, Hajari K and Golhar Y 2023 Real-Time Deep Learning Approach for Pedestrian Detection and Suspicious Activity Recognition. Procedia Comput Sci 2182438–2447. https://doi.org/10.1016/j.procs.2023.01.219
Ge C, Wang J, Wang J, et al 2020 Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection. Comput Ind 121103232. https://doi.org/10.1016/j.compind.2020.103232
Ge Z, Wang J, Huang X, et al 2021 LLA: Loss-aware label assignment for dense pedestrian detection. Neurocomputing 462272–281. https://doi.org/10.1016/j.neucom.2021.07.094
Geiger A, Lenz P and Urtasun R 2012 Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 3354–3361
Ghiasi G, Lin T-Y and Le Q V. 2019 NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 7029–7038
Girshick R 2015 Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 1440–1448
Girshick R, Donahue J, Darrell T and Malik J 2014 Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 580–587
Girshick R, Donahue J, Darrell T, Malik J (2015) Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158. https://doi.org/10.1109/TPAMI.2015.2437384
Goldman E, Herzig R, Eisenschtat A et al (2019) Precise Detection in Densely Packed Scenes. Comput Speech Lang 68101200
Grosicki E, El-Abed H 2011 ICDAR 2011 - French Handwriting Recognition Competition. In: 2011 International Conference on Document Analysis and Recognition. IEEE, 1459–1463
Gu WH, Zhu Y, Chen XD et al (2018) Hierarchical CNN-based real-time fatigue detection system by visual-based technologies using MSP model. IET Image Process 12(12):2319–2329. https://doi.org/10.1049/iet-ipr.2018.5245
Guo Z, Yang G, Chen J, Sun X (2021) Fake face detection via adaptive manipulation traces extraction network. Comput Vis Image Underst 204(January):103170. https://doi.org/10.1016/j.cviu.2021.103170
Guo Z, Liao W, Xiao Y, et al 2021 Weak segmentation supervised deep neural networks for pedestrian detection. Pattern Recognit 119108063. https://doi.org/10.1016/j.patcog.2021.108063
Gupta A, Anpalagan A, Guan L and Khwaja AS 2021 Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues. Array 10(September 2020): 100057. https://doi.org/10.1016/j.array.2021.100057
Gupta S, Thakur K, Kumar M (2021) 2D-human face recognition using SIFT and SURF descriptors of face’s feature regions. Vis Comput 37(3):447–456. https://doi.org/10.1007/s00371-020-01814-8
Han K, Wang Y, Tian Q, et al. 2020 GhostNet: More Features From Cheap Operations. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1577–1586
Han J, Ding J, Xue N and Xia G-S 2021 ReDet: A Rotation-equivariant Detector for Aerial Object Detection. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2785–2794
Hangaragi S, Singh T and Neelima N 2023 Face Detection and Recognition Using Face Mesh and Deep Neural Network. Procedia Comput Sci 218741–749. https://doi.org/10.1016/j.procs.2023.01.054
Hanyao M, Jin Y, Qian Z, et al 2021 Edge-assisted Online On-device Object Detection for Real-time Video Analytics. In: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications. IEEE, 1–10
Hao Z, Liu Y, Qin H, et al. 2017 Scale-Aware Face Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1913–1922
Hasan M, Orgun MA, Schwitter R (2018) A survey on real-time event detection from the Twitter data stream. J Inf Sci 44(4):443–463. https://doi.org/10.1177/0165551517698564
He K, Zhang X, Ren S, Sun J (2015) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916. https://doi.org/10.1109/TPAMI.2015.2389824
He K, Zhang X, Ren S and Sun J 2016 Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016-Decem770–778. https://doi.org/10.1109/CVPR.2016.90
He S, Liang G, Chen F, et al 2018 Object Recognition and 3D Pose Estimation Using Improved VGG16 Deep Neural Network in Cluttered Scenes. In: Proceedings of the International Conference on Information Technology and Electrical Engineering 2018. ACM, New York, NY, USA, pp. 1–7
He K, Gkioxari G, Dollar P, Girshick R (2020) Mask R-CNN. IEEE Trans Pattern Anal Mach Intell 42(2):386–397. https://doi.org/10.1109/TPAMI.2018.284
He W, Zhang X-Y, Yin F, et al 2020 Realtime multi-scale scene text detection with scale-based region proposal network. Pattern Recognit 981–14. https://doi.org/10.1016/j.patcog.2019.107026
He Z, Nan F, Li X et al (2020) Traffic sign recognition by combining global and local features based on semi-supervised classification. IET Intell Transp Syst 14(5):323–330. https://doi.org/10.1049/iet-its.2019.0409
Hechun W and Xiaohong Z 2019 Survey of Deep Learning Based Object Detection. In: Proceedings of the 2nd International Conference on Big Data Technologies - ICBDT2019. ACM Press, New York, New York, USA, pp. 149–153
Heitz G, Koller D (2008) Learning Spatial Context: Using Stuff to Find Things. In: European Conference on Computer Vision. Springer, Berlin, Heidelberg, pp 30–43
Hinton GE, Sejnowski TE (1986) Learning and relearning in Boltzmann machines. In Parallel Distributed Processing. Parallel Distrib Process 1:282–317
Hosni Mahmoud HA, Mengash HA (2021) A novel technique for automated concealed face detection in surveillance videos. Pers Ubiquit Comput 25(1):129–140. https://doi.org/10.1007/s00779-020-01419-x
Houben S, Stallkamp J, Salmen J, et al 2013 Detection of traffic signs in real-world images: The German traffic sign detection benchmark. In: The 2013 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8
Hu P and Ramanan D 2017 Finding Tiny Faces. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 1522–1530
Hu K, Yang W and Gao X 2017 Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform. Expert Syst Appl 86135–144. https://doi.org/10.1016/j.eswa.2017.05.062
Hu Y, Wu X, Zheng G and Liu X 2019 Object Detection of UAV for Anti-UAV Based on Improved YOLO v3. In: 2019 Chinese Control Conference (CCC). IEEE, pp. 8386–8390
Hu J, Shen L, Albanie S et al (2020) Squeeze-and-Excitation Networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011–2023. https://doi.org/10.1109/TPAMI.2019.2913372
Hu J, Zhao Y and Zhang X 2020 Application of Transfer Learning in Infrared Pedestrian Detection. In: 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC). IEEE, pp. 1–4
Hua X, Wang X, Rui T, et al 2020 A fast self-attention cascaded network for object detection in large scene remote sensing images. Appl Soft Comput 94106495. https://doi.org/10.1016/j.asoc.2020.106495
Huang J, Rathod V, Sun C, et al 2017 Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 3296–3297
Huang H, Zhou H, Yang X, et al 2019 Faster R-CNN for marine organisms detection and recognition using data augmentation. Neurocomputing 337372–384. https://doi.org/10.1016/j.neucom.2019.01.084
Huang W, Luo M, Liu X et al (2019) Arterial Spin Labeling Images Synthesis from sMRI Using Unbalanced Deep Discriminant Learning. IEEE Trans Med Imaging 38(10):2338–2351. https://doi.org/10.1109/TMI.2019.2906677
Huang Z, Chen K, He J, et al 2019 ICDAR2019 competition on scanned receipt OCR and information extraction. Proc Int Conf Doc Anal Recognition, ICDAR 1516–1520. https://doi.org/10.1109/ICDAR.2019.00244
Huang Q, Wang D, Dong Z, et al 2021 CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs. In: The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, New York, NY, USA, pp. 206–216
Huang Q, Cai Z and Lan T 2021 A Single Neural Network for Mixed Style License Plate Detection and Recognition. IEEE Access 921777–21785. https://doi.org/10.1109/ACCESS.2021.3055243
Hung BT 2021 Face Recognition Using Hybrid HOG-CNN Approach. In: International Journal of Image and Graphics. Springer, pp. 715–723
Hung GL, Bin SMS, Samma H et al (2020) Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images. SN Comput Sci 1(2):116. https://doi.org/10.1007/s42979-020-00125-y
Irbaz MS, Al Nasim MA and Ferdous RE 2022 Real-time Face Recognition System for Remote Employee Tracking. In: Lecture Notes on Data Engineering and Communications Technologies. Springer, 153–163
Ivašić-Kos M, Krišto M and Pobar M 2019 Human Detection in Thermal Imaging Using YOLO. In: Proceedings of the 2019 5th International Conference on Computer and Technology Applications. ACM, New York, NY, USA, pp. 20–24
Jaderberg M, Simonyan K, Vedaldi A and Zisserman A 2014 Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition, pp. 1–10
Jaiswal D, Kumar P (2020) Real-time implementation of moving object detection in UAV videos using GPUs. J Real-Time Image Proc 17(5):1301–1317. https://doi.org/10.1007/s11554-019-00888-5
Jamtsho Y, Riyamongkol P, Waranusast R (2021) Real-time license plate detection for non-helmeted motorcyclist using YOLO. ICT Express 7(1):104–109. https://doi.org/10.1016/j.icte.2020.07.008
Jani D and Mankodia A 2021 Comprehensive Analysis of Object Detection And Tracking Methodologies From Surveillance Videos. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, pp. 963–970
Jiafa M, Weifeng W, Yahong H, Weiguo S (2019) A scene recognition algorithm based on deep residual network. Syst Sci Control Eng 7(1):243–251. https://doi.org/10.1080/21642583.2019.1647576
Jian M, Wang J, Yu H et al (2021) Visual saliency detection by integrating spatial position prior of object with background cues. Expert Syst Appl 168(November):114219. https://doi.org/10.1016/j.eswa.2020.114219
Jiang Z, Huynh DQ (2018) Multiple Pedestrian Tracking From Monocular Videos in an Interacting Multiple Model Framework. IEEE Trans Image Process 27(3):1361–1375. https://doi.org/10.1109/TIP.2017.2779856
Jiang S, Jin H and Wei F 2013 LS-SVM application for ship course model predictive control. In: 2013 IEEE International Conference on Mechatronics and Automation. IEEE, pp. 1615–1619
Jiao L, Zhang F, Liu F, et al 2019 A Survey of Deep Learning-Based Object Detection. IEEE Access, pp. 71–33. https://doi.org/10.1109/ACCESS.2019.2939201
Jiao L, Dong S, Zhang S et al (2020) AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection. Comput Electron Agric 174(April):105522. https://doi.org/10.1016/j.compag.2020.105522
Jin J, Zhu A, Wang Y, Wright J (2021) A feature binding model in computer vision for object detection. Multimed Tools Appl 80(13):19377–19397. https://doi.org/10.1007/s11042-021-10702-9
Jin Y, Zhang Y, Cen Y, et al 2021 Pedestrian detection with super-resolution reconstruction for low-quality image. Pattern Recognit 115107846. https://doi.org/10.1016/j.patcog.2021.107846
Jose A, Thodupunoori H and Nair BB 2019 A Novel Traffi 17
Joseph KJ, Khan S, Khan FS and Balasubramanian VN 2021 Towards Open World Object Detection. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 5826–5836
Kalyanam J, Katsuki T, R.G. Lanckriet G and Mackey TK 2017 Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning. Addict Behav 65(509): 289–295. https://doi.org/10.1016/j.addbeh.2016.08.019
Kaplan C, Bulbul A (2021) Goal driven network pruning for object recognition. Pattern Recogn 110(1):1–11. https://doi.org/10.1016/j.patcog.2020.107468
Karatzas D, Mestre SR, Mas J, et al 2011 ICDAR 2011 Robust Reading Competition - Challenge 1: Reading Text in Born-Digital Images (Web and Email). In: 2011 International Conference on Document Analysis and Recognition. IEEE, pp. 1485–1490
Kaur J, Singh W (2021) An Approach Towards Indian Road Sign Detection System Using Deep Learning. In: In: 3rd International Conference on Innovative Trends in Electronics Engineering. Royal Book Publishing (Partner of Eleyon Publishers), pp 92–101
Kaur J, Singh W (2022) Tools, techniques, datasets and application areas for object detection in an image: a review. Multimed Tools Appl 81(27):38297–38351. https://doi.org/10.1007/s11042-022-13153-y
Kaur RP, Kumar M and Jindal MK 2022 Performance evaluation of different features and classifiers for Gurumukhi newspaper text recognition. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03687-8
Khan A, Rinner B, Cavallaro A (2018) Cooperative Robots to Observe Moving Targets: Review. IEEE Trans Cybern 48(1):187–198. https://doi.org/10.1109/TCYB.2016.2628161
Kilic E, Ozturk S (2019) A subclass supported convolutional neural network for object detection and localization in remote-sensing images. Int J Remote Sens 40(11):4193–4212. https://doi.org/10.1080/01431161.2018.1562260
Kim Y, Hwang H, Shin J (2021) Robust object detection under harsh autonomous-driving environments. IET Image Process 16(4):958–971. https://doi.org/10.1049/ipr2.12159
Klare BF, Klein B, Taborsky E, et al 2015 Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 1931–1939
Kostinger M, Wohlhart P, Roth PM and Bischof H 2011 Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, pp. 2144–2151
Kousik N, Natarajan Y, Arshath Raja R, et al 2021 Improved salient object detection using hybrid Convolution Recurrent Neural Network. Expert Syst Appl, pp. 1661–33. https://doi.org/10.1016/j.eswa.2020.114064
Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386
Kulik S, Shtanko A (2020) Using convolutional neural networks for recognition of objects varied in appearance in computer vision for intellectual robots. Procedia Comput Sci 169(2019):164–167. https://doi.org/10.1016/j.procs.2020.02.129
Kumar A, Srivastava S (2020) Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector. Procedia Comput Sci 171(2019):2610–2617. https://doi.org/10.1016/j.procs.2020.04.283
Kumar A, Kumar M, Kaur A (2021) Face detection in still images under occlusion and non-uniform illumination. Multimed Tools Appl 80(10):14565–14590. https://doi.org/10.1007/s11042-020-10457-9
Kuznetsova A, Rom H, Alldrin N et al (2020) The Open Images Dataset V4. Int J Comput Vis 128(7):1956–1981. https://doi.org/10.1007/s11263-020-01316-z
Kuznetsova A, Maleva T and Soloviev V 2020 Detecting Apples in Orchards Using YOLOv3 and YOLOv5 in General and Close-Up Images. In: Neurocomputing. Springer, pp. 233–243
Lam D, Kuzma R, McGee K, et al 2018 xView: Objects in Context in Overhead Imagery. arxiv
Lan W, Dang J, Wang Y and Wang S 2018 Pedestrian detection based on yolo network model. In: Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018. IEEE, pp. 1547–1551
Laroca R, Zanlorensi LA, Gonçalves GR et al (2021) An efficient and layout-independent automatic license plate recognition system based on the YOLO detector. IET Intell Transp Syst 15(4):1–21. https://doi.org/10.1049/itr2.12030
Law H, Deng J (2020) CornerNet: Detecting Objects as Paired Keypoints. Int J Comput Vis 128(3):642–656. https://doi.org/10.1007/s11263-019-01204-1
Lazebnik S, Schmid C and Ponce J 2006 Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR’06). IEEE, 2169–2178
Learned-Miller E and Jain V 2010 FDDB: A Benchmark for Face Detection in Unconstrained Settings
Lee D-H (2021) CNN-based single object detection and tracking in videos and its application to drone detection. Multimed Tools Appl 80(26–27):34237–34248. https://doi.org/10.1007/s11042-020-09924-0
Leira FS, Helgesen HH, Johansen TA, Fossen TI (2021) Object detection, recognition, and tracking from UAVs using a thermal camera. J F Robot 38(2):242–267. https://doi.org/10.1002/rob.21985
Leksut JT, Zhao J, Itti L (2020) Learning visual variation for object recognition. Image Vis Comput 98103912. https://doi.org/10.1016/j.imavis.2020.103912
Li J, Liang X, Wei Y, et al 2017 Perceptual Generative Adversarial Networks for Small Object Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 1951–1959
Li Y, Zhang Y, Huang X and Yuille AL 2018 Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images. ISPRS J Photogramm Remote Sens, pp. 1461–30. https://doi.org/10.1016/j.isprsjprs.2018.09.014
Li Z, Peng C, Yu G, et al 2018 DetNet: A Backbone network for Object Detection. 1–17
Li K, Wan G, Cheng G et al (2019) 2020 Object detection in optical remote sensing images: A survey and a new benchmark. ISPRS J Photogramm Remote Sens 159:296–307. https://doi.org/10.1016/j.isprsjprs.2019.11.023
Li Y, Chen Y, Wang N and Zhang Z-X 2019 Scale-Aware Trident Networks for Object Detection. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, pp. 6053–6062
Li C, Cong R, Guo C, et al 2020 A parallel down-up fusion network for salient object detection in optical remote sensing images. Neurocomputing, pp. 415411–420. https://doi.org/10.1016/j.neucom.2020.05.108
Li F, Luo Z, Huang J, et al 2020 AlTwo: Vehicle Recognition in Foggy Weather Based on Two-Step Recognition Algorithm. In: Neurocomputing. Springer, pp. 130–141
Li X, Li Y and Li S 2020 Recent Advances of Generic Object Detection with Deep Learning: A Review. In: Need more specific jurnal, pp. 185–193
Li X, Luo M, Ji S et al (2020) Evaluating generative adversarial networks based image-level domain transfer for multi-source remote sensing image segmentation and object detection. Int J Remote Sens 41(19):7343–7367. https://doi.org/10.1080/01431161.2020.1757782
Li Y, Dong H, Li H et al (2020) Multi-block SSD based on small object detection for UAV railway scene surveillance. Chin J Aeronaut 33(6):1747–1755. https://doi.org/10.1016/j.cja.2020.02.024
Li B, Xie X, Wei X, Tang W (2021) Ship detection and classification from optical remote sensing images: A survey. Chin J Aeronaut 34(3):145–163. https://doi.org/10.1016/j.cja.2020.09.022
Li G, Huang X, Ai J et al (2021) Lemon-YOLO: An efficient object detection method for lemons in the natural environment. IET Image Process 15(9):1998–2009. https://doi.org/10.1049/ipr2.12171
Li C, Li L, Jiang H, et al 2022 YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv
Li Z, Liu F, Yang W et al (2022) A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Trans Neural Networks Learn Syst 33(12):6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827
Liao F, Liang M, Li Z et al (2019) Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network. IEEE Trans Neural Networks Learn Syst 30(11):3484–3495. https://doi.org/10.1109/TNNLS.2019.2892409
Liao J, Liu Y, Piao Y et al (2022) GLE-Net: A Global and Local Ensemble Network for Aerial Object Detection. Int J Comput Intell Syst 15(1):2. https://doi.org/10.1007/s44196-021-00056-3
Lin TY, Maire M, Belongie S et al (2014) Microsoft COCO: Common objects in context. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 8693 LNCS (PART 5): 740–755. https://doi.org/10.1007/978-3-319-10602-1_48
Lin T-Y, Dollar P, Girshick R, et al 2017 Feature Pyramid Networks for Object Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 936–944
Lin T-Y, Goyal P, Girshick R, et al 2017 Focal Loss for Dense Object Detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, pp. 2999–3007
Lin M, Chen C, Lai C (2019) Object detection algorithm based AdaBoost residual correction Fast R-CNN on network. ACM Int Conf Proc Ser 22:42–46. https://doi.org/10.1145/3342999.3343013
Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42(October):60–88. https://doi.org/10.1016/j.media.2017.07.005
Liu K, Mattyus G (2015) Fast Multiclass Vehicle Detection on Aerial Images. IEEE Geosci Remote Sens Lett 12(9):1938–1942. https://doi.org/10.1109/LGRS.2015.2439517
Liu W, Anguelov D, Erhan D et al (2016) SSD: Single Shot MultiBox Detector. In: European conference on computer vision. Springer, pp 21–37
Liu Z, Wang H, Weng L, Yang Y (2016) Ship Rotated Bounding Box Space for Ship Extraction From High-Resolution Optical Satellite Images With Complex Backgrounds. IEEE Geosci Remote Sens Lett 13(8):1074–1078. https://doi.org/10.1109/LGRS.2016.2565705
Liu Z, Wang D, Lu H (2017) Stepwise Metric Promotion for Unsupervised Video Person Re-identification. In: In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 2448–2457
Liu Y, Jin L, Zhang S et al (2019) Curved scene text detection via transverse and longitudinal sequence connection. Pattern Recogn 90337–345. https://doi.org/10.1016/j.patcog.2019.02.002
Liu D, Cui Y, Chen Y, et al 2020 Video object detection for autonomous driving: Motion-aid feature calibration. Neurocomputing 4091–11. https://doi.org/10.1016/j.neucom.2020.05.027
Liu L, Ouyang W, Wang X et al (2020) Deep Learning for Generic Object Detection: A Survey. Int J Comput Vis 128(2):261–318. https://doi.org/10.1007/s11263-019-01247-4
Liu Y, Duanmu M, Huo Z, et al 2021 Exploring multi-scale deformable context and channel-wise attention for salient object detection. Neurocomputing 42892–103. https://doi.org/10.1016/j.neucom.2020.11.022
Liu JJ, Hou Q, Liu ZA, Cheng MM (2022) PoolNet+: Exploring the Potential of Pooling for Salient Object Detection. IEEE Trans Pattern Anal Mach Intell 45(1):1–18. https://doi.org/10.1109/TPAMI.2021.3140168
Liu Y, Liu J, Ning X, Li J (2022) MS-CNN: multiscale recognition of building rooftops from high spatial resolution remote sensing imagery. Int J Remote Sens 43(1):270–298. https://doi.org/10.1080/01431161.2021.2018146
Loey M, Manogaran G, Taha MHN and Khalifa NEM 2021 Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection. Sustain Cities Soc 65102600. https://doi.org/10.1016/j.scs.2020.102600
Lu Y, Lu J, Zhang S, Hall P (2018) Traffic signal detection and classification in street views using an attention model. Comput Vis Media 4(3):253–266. https://doi.org/10.1007/s41095-018-0116-x
Lu S, Wang B, Wang H, et al. 2019 A real-time object detection algorithm for video. Comput Electr Eng 77398–408. https://doi.org/10.1016/j.compeleceng.2019.05.009
Lu W, Zhou Y, Wan G, et al. 2019 L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 6382–6391
Lu X, Ji J, Xing Z, Miao Q (2021) Attention and feature fusion SSD for remote sensing object detection. IEEE Trans Instrum Meas 70. https://doi.org/10.1109/TIM.2021.3052575
Lucas SM 2005 ICDAR 2005 text locating competition results. In: Eighth International Conference on Document Analysis and Recognition (ICDAR’05). IEEE, pp. 80–84 Vol. 1
Lucas SM, Panaretos A, Sosa L, et al 2003 ICDAR 2003 robust reading competitions. In: Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings. IEEE, pp. 682–687
Luo X, Zhu J, Yu Q (2019) Efficient convNets for fast traffic sign recognition. IET Intell Transp Syst 13(6):1011–1015. https://doi.org/10.1049/iet-its.2018.5489
Lv X, Su M and Wang Z 2021 Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems. Microprocess Microsyst 104034. https://doi.org/10.1016/j.micpro.2021.104034
Ma W, Wu Y, Cen F, Wang G (2020) MDFN: Multi-scale deep feature learning network for object detection. Pattern Recognit 100:107149. https://doi.org/10.1016/j.patcog.2019.107149
Ma C, Sun L, Zhong Z and Huo Q 2021 ReLaText: Exploiting visual relationships for arbitrary-shaped scene text detection with graph convolutional networks. Pattern Recognit 111:107684. https://doi.org/10.1016/j.patcog.2020.107684
Ma J, Liang M, Chen S-L, et al 2022 Depth-Guided Progressive Network for Object Detection. IEEE Trans Intell Transp Syst 1–11. https://doi.org/10.1109/TITS.2022.3156365
Maeda H, Kashiyama T, Sekimoto Y et al (2021) Generative adversarial network for road damage detection. Comput Civ Infrastruct Eng 36(1):1–14. https://doi.org/10.1111/mice.12561
Majumdar P, Agarwal A, Singh R and Vatsa M 2019 Evading Face Recognition via Partial Tampering of Faces. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 11–20
Masita KL, Hasan AN, Shongwe T (2022) Refining the Efficiency of R-CNN in Pedestrian Detection. In: Lecture Notes in Networks and Systems. Springer, pp 1–14
Maze B, Adams J, Duncan JA et al (2018) IARPA Janus Benchmark - C: Face Dataset and Protocol. In: In: 2018 International Conference on Biometrics (ICB). IEEE, pp 158–165
Mehedi Shamrat FMJ, Al JM, Billah MM et al (2021) A Deep Learning Approach for Face Detection using Max Pooling. In: In: 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, pp 760–764
Mishra A, Alahari K, Jawahar C (2012) Scene Text Recognition using Higher Order Language Priors. In: Procedings of the British Machine Vision Conference 2012. British Mach Vision Assoc 127:1–127.11
Misra I, Girdhar R, Joulin A (2021) An End-to-End Transformer Model for 3D Object Detection. In: In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, pp 2886–2897
Mittal U, Srivastava S and Chawla P 2019 Review of different techniques for object detection using deep learning. In: Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR ‘19. ACM Press, New York, New York, USA, pp. 1–8
Mittal P, Singh R and Sharma A 2020 Deep learning-based object detection in low-altitude UAV datasets: A survey. Image Vis Comput 104:104046. https://doi.org/10.1016/j.imavis.2020.104046
Mogelmose A, Trivedi MM, Moeslund TB (2012) Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey. IEEE Trans Intell Transp Syst 13(4):1484–1497. https://doi.org/10.1109/TITS.2012.2209421
Murdock M, Reid S, Hamilton B and Reese J 2015 ICDAR 2015 competition on text line detection in historical documents. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, pp. 1171–1175
Nada H, Sindagi VA, Zhang H and Patel VM 2018 Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, pp. 1–10
Naiemi F (2020) Ghods V and Khalesi H 2021 A novel pipeline framework for multi oriented scene text image detection and recognition. Expert Syst Appl 170:114549. https://doi.org/10.1016/j.eswa.2020.114549
Najibi M, Samangouei P, Chellappa R and Davis LS 2017 SSH: Single Stage Headless Face Detector. In: Proceedings of the IEEE International Conference on Computer Vision. IEEE, 4885–4894
Natarajan S, Annamraju AK, Baradkar CS (2018) Traffic sign recognition using weighted multi-convolutional neural network. IET Intell Transp Syst 12(10):1396–1405. https://doi.org/10.1049/iet-its.2018.5171
Neumann L, Karg M, Zhang S et al (2019) NightOwls: A Pedestrians at Night Dataset. In: Li H, Mori G, Schindler K (eds) Computer Vision – ACCV 2018. Springer International Publishing, pp 691–705
Nguyen DT, Li W and Ogunbona PO 2016 Human detection from images and videos: A survey. Pattern Recognit 51148–175. https://doi.org/10.1016/j.patcog.2015.08.027
Nguyen VN, Jenssen R, Roverso D (2019) Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning. IEEE Power Energy Technol Syst J 6(1):11–21. https://doi.org/10.1109/jpets.2018.2881429
Nie GY, Cheng MM, Liu Y, et al 2019 Multi-level context ultra-aggregation for stereo matching. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 3278–3286
Ogura R, Nagasaki T, Matsubara H (2020) Improving the visibility of nighttime images for pedestrian recognition using in-vehicle camera. Electron Commun Japan 103(10):35–43. https://doi.org/10.1002/ecj.12268
Oksuz K, Cam BC, Kalkan S and Akbas E 2019 Imbalance problems in object detection: A review. arXiv 8828(c):. https://doi.org/10.1109/tpami.2020.2981890
Ota K, Dao MS, Mezaris V, De Natale FGB (2017) Deep learning for mobile multimedia: A survey. ACM Trans Multimed Comput Commun Appl 13(3s):1–22. https://doi.org/10.1145/3092831
Ouyang W, Wang X, Zeng X, et al 2015 DeepID-Net: Deformable deep convolutional neural networks for object detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2403–2412
Padilla R, Netto SL and da Silva EABB 2020 A Survey on Performance Metrics for Object-Detection Algorithms. In: 2020 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 237–242
Pang Y, Cao J, Li Y, et al 2021 TJU-DHD: A Diverse High-Resolution Dataset for Object Detection. IEEE Trans Image Process 30207–219. https://doi.org/10.1109/TIP.2020.3034487
Papageorgiou C, Poggio T (2000) Trainable system for object detection. Int J Comput Vis 38(1):15–33. https://doi.org/10.1023/A:1008162616689
Pathak AR, Pandey M, Rautaray S (2018) Application of Deep Learning for Object Detection. Procedia Comput Sci 132(June):1706–1717. https://doi.org/10.1016/j.procs.2018.05.144
Paul S (1986) Information processing in dynamical systems: foundations of harmony theory. J Japan Soc Fuzzy Theory Syst 4(2):194–281
Pérez-Hernández F, Tabik S, Lamas A, et al 2020 Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance. Knowledge-Based Syst 1941–10. https://doi.org/10.1016/j.knosys.2020.105590
Perronnin F, Sánchez J and Mensink T 2010 Improving the Fisher Kernel for Large-Scale Image Classification. In: Journal of AOAC INTERNATIONAL. Springer, pp. 143–156
Phung and Rhee (2019) A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets. Appl Sci 9(21):4500. https://doi.org/10.3390/app9214500
Pouyanfar S, Sadiq S, Yan Y et al (2019) A Survey on Deep learning: Algorithmm Techniques and Applications. ACM Comput Surv 51(5):1–36. https://doi.org/10.1145/3234150
Qi Q, Tan W, Zhang K, Huang M (2018) Object detection with multi-RCNN detectors. In: ACM International Conference Proceeding Series. ACM, New York, NY, USA, pp 193–197
Qin S and Liu S 2021 Towards end-to-end car license plate location and recognition in unconstrained scenarios. Neural Comput Appl 1–11. https://doi.org/10.1007/s00521-021-06147-8
Qiu Z, Zhu X, Liao C et al (2021) Detection of bird species related to transmission line faults based on lightweight convolutional neural network. IET Gener Transm Distrib 16(5):869–881. https://doi.org/10.1049/gtd2.12333
Rahman MM, Tan Y, Xue J, Lu K (2020) Recent Advances in 3D Object Detection in the Era of Deep Neural Networks: A Survey. IEEE Trans Image Process 29(8):2947–2962. https://doi.org/10.1109/TIP.2019.2955239
Rahman MM, Al MS, Kaiser MS et al (2021) Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement. In: Advances in Intelligent Systems and Computing. Springer, pp 581–590
Rahmaniar W, Hernawan A (2021) Real-Time Human Detection Using Deep Learning on Embedded Platforms: A Review | Rahmaniar |. J Robot Control (JRC) J Robot Cont 2(6):462–468. https://doi.org/10.18196/jrc.26123
Ramzi M, Larbi G and Lyamine G 2019 Road obstacle detection. ACM Int Conf Proceeding Ser. https://doi.org/10.1145/3341325.3341999
Ravishankar V, Vinod V, Kumar T and Bhalla K 2022 Sensor Integration and Facial Recognition Deployment in a Smart Home System. Springer, 759–771
Razakarivony S and Jurie F 2016 Vehicle detection in aerial imagery: A small target detection benchmark. J Vis Commun Image Represent 34187–203. https://doi.org/10.1016/j.jvcir.2015.11.002
Redmon J and Farhadi A 2017 YOLO9000: Better, Faster, Stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 6517–6525
Redmon J and Farhadi A 2018 YOLOv3: An Incremental Improvement. Comput Vis Pattern Recognit 1–6
Redmon J, Divvala S, Girshick R and Farhadi A 2016 You Only Look Once: Unified, Real-Time Object Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 779–788
Rehman ZU, Zia MS, Bojja GR et al (2020) Texture based localization of a brain tumor from MR-images by using a machine learning approach. Med Hypotheses 141(March):109705. https://doi.org/10.1016/j.mehy.2020.109705
Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
Ren J, Ren M, Liu R et al (2021) An Effective Imaging System for 3D Detection of Occluded Objects. In: 2021 The 4th International Conference on Image and Graphics Processing. ACM, New York, NY, USA, pp 20–30
Renu Chebrolu KN and Kumar PN 2019 Deep Learning based Pedestrian Detection at all Light Conditions. In: 2019 International Conference on Communication and Signal Processing (ICCSP). IEEE, pp. 1–5
Risnumawan A, Shivakumara P, Chan CS, Tan CL (2014) A robust arbitrary text detection system for natural scene images. Expert Syst Appl 41(18):8027–8048. https://doi.org/10.1016/j.eswa.2014.07.008
Rukhovich D, Vorontsova A and Konushin A 2022 ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, pp. 1265–1274
Sabu K and Rao P 2021 Prosodic event detection in children’s read speech. Comput Speech Lang 68101200. https://doi.org/10.1016/j.csl.2021.101200
Sahiner B, Pezeshk A, Hadjiiski LM et al (2019) Deep learning in medical imaging and radiation therapy. Med Phys 46(1):e1–e36. https://doi.org/10.1002/mp.13264
Sai Srinath NGS, Joseph AZ, Umamaheswaran S et al (2020) NITCAD - Developing an object detection, classification and stereo vision dataset for autonomous navigation in Indian roads. Procedia Comput Sci 171(2019):207–216. https://doi.org/10.1016/j.procs.2020.04.022
Saleh K, Szenasi S and Vamossy Z 2021 Occlusion Handling in Generic Object Detection: A Review. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 000477–000484
Sanchez JA, Toselli AH, Romero V and Vidal E 2015 ICDAR 2015 competition HTRtS: Handwritten Text Recognition on the tranScriptorium dataset. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, pp. 1166–1170
Sanchez JA, Romero V, Toselli AH, et al 2017 ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, pp. 1383–1388
Schöller FET, Plenge-Feidenhans’L MK, Stets JD and Blanke M 2019 Assessing Deep-learning Methods for Object Detection at Sea from LWIR Images. In: IFAC-PapersOnLine. Elsevier Ltd, pp. 64–71
Sermanet P, Eigen D, Zhang X, et al 2014 Overfeat: Integrated recognition, localization and detection using convolutional networks. 2nd Int Conf Learn Represent ICLR 2014 - Conf Track Proc
Setta S, Sinha S, Mishra M and Choudhury P 2022 Real-Time Facial Recognition Using SURF-FAST. In: Lecture Notes on Data Engineering and Communications Technologies, pp. 505–522
Shahab A, Shafait F and Dengel A 2011 ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images. In: 2011 International Conference on Document Analysis and Recognition. IEEE, pp. 1491–1496
Shao Z, Cheng G, Ma J, et al 2021 Real-time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic. IEEE Trans Multimed 1–1. https://doi.org/10.1109/TMM.2021.3075566
Sharma V, Mir RN (2019) Saliency guided faster-RCNN (SGFr-RCNN) model for object detection and recognition. J King Saud Univ - Comput Inf Sci 34(5):0–12. https://doi.org/10.1016/j.jksuci.2019.09.012
Sharma V and Mir RN 2020 A comprehensive and systematic look up into deep learning based object detection techniques: A review. Comput Sci Rev 38:100301. https://doi.org/10.1016/j.cosrev.2020.100301
Sharma N, Mandal R, Sharma R, et al 2015 ICDAR2015 Competition on Video Script Identification (CVSI 2015). In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, pp. 1196–1200
Shashirangana J, Padmasiri H, Meedeniya D, et al 2021 License plate recognition using neural architecture search for edge devices. Int J Intell Syst 1–38. https://doi.org/10.1002/int.22471
Shen ZY, Han SY, Fu LC, et al 2019 Deep convolution neural network with scene-centric and object-centric information for object detection. Image Vis Comput 85:14–25. https://doi.org/10.1016/j.imavis.2019.03.004
Shepley AJ, Falzon G, Kwan P and Brankovic L 2023 Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection. IEEE Trans Pattern Anal Mach Intell 1–16. https://doi.org/10.1109/TPAMI.2023.3273210
Shi X, Shan S, Kan M, et al 2018 Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, pp. 2295–2303
Shi Y, Zhang Z, Huang K, et al 2020 Human-computer interaction based on face feature localization. J Vis Commun Image Represent 70:1–6. https://doi.org/10.1016/j.jvcir.2019.102740
Shrivastava A, Sukthankar R, Malik J and Gupta A 2016 Beyond Skip Connections: Top-Down Modulation for Object Detection
Shyu M, Chen S, Iyengar SS (2020) A survey on deep learning algorithms, techniques, and applications. Strad Res 7(8). https://doi.org/10.37896/sr7.8/037
Siebert FW, Lin H (2020) Detecting motorcycle helmet use with deep learning. Accid Anal Prev 134(May 2019):105319. https://doi.org/10.1016/j.aap.2019.105319
Simonyan K and Zisserman A 2015 Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. arXiv, 1–14
Singh S and Prasad SVAV 2018 Techniques and challenges of face recognition: A critical review. Procedia Comput Sci 143:536–543. https://doi.org/10.1016/j.procs.2018.10.427
Song H, Wang W, Zhao S, et al. 2018 Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection. In: Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, pp. 744–760
Song X, Wang P, Zhou D, et al 2019 APOLLOCAR3D: A large 3D car instance understanding benchmark for autonomous driving. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2019-June, pp. 5447–5457. https://doi.org/10.1109/CVPR.2019.00560
Sri Jamiya S and Rani PE 2021 LittleYOLO-SPP: A delicate real-time vehicle detection algorithm. Optik (Stuttg) 225:165818. https://doi.org/10.1016/j.ijleo.2020.165818
Su H, Wei S, Yan M, et al 2019 Object Detection and Instance Segmentation in Remote Sensing Imagery Based on Precise Mask R-CNN. In: IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 1454–1457
Sun S, Yin Y, Wang X et al (2018) Fast object detection based on binary deep convolution neural networks. CAAI Trans Intell Technol 3(4):198–207. https://doi.org/10.1049/trit.2018.1026
Sun K, Xiao B, Liu D, Wang J (2019) Deep High-Resolution Representation Learning for Human Pose Estimation. In: In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 5686–5696
Sun K, Zhao Y, Jiang B, et al. 2019 High-Resolution Representations for Labeling Pixels and Regions
Sun Y, Karatzas D, Chan CS, et al 2019 ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling - RRC-LSVT. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, pp. 1557–1562
Sun P, Zheng Y, Zhou Z, et al. 2020 R4 Det: Refined single-stage detector with feature recursion and refinement for rotating object detection in aerial images. Image Vis Comput 1031–26. https://doi.org/10.1016/j.imavis.2020.104036
Sun F, Li H, Liu Z et al (2021) Arbitrary-angle bounding box based location for object detection in remote sensing image. Eur J Remote Sens 54(1):102–116. https://doi.org/10.1080/22797254.2021.1880975
Sun X, Wang P, Wang C, et al 2021 PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery. ISPRS J Photogramm Remote Sens 173:50–65. https://doi.org/10.1016/j.isprsjprs.2020.12.015
Suzuki T, Kageyama Y, Ishizawa C (2020) Recognition Method for Speed Limit Signs and its Applicability in Recognition of Vehicle Entry Prohibition Signs at Night. IEEJ Trans Electr Electron Eng 15(10):1–9. https://doi.org/10.1002/tee.23215
Tamilselvi M, Karthikeyan S (2022) An ingenious face recognition system based on HRPSM_CNN under unrestrained environmental condition. Alexandria Eng J 61(6):4307–4321. https://doi.org/10.1016/j.aej.2021.09.043
Tan M, Pang R and Le Q V. 2019 EfficientDet: Scalable and efficient object detection. arXiv 10781–10790
Tang S, Roberts D, Golparvar-Fard M (2020) Human-object interaction recognition for automatic construction site safety inspection. Autom Constr 120(July):1–16. https://doi.org/10.1016/j.autcon.2020.103356
Tanner F, Colder B, Pullen C, et al 2009 Overhead imagery research data set — an annotated data library & tools to aid in the development of computer vision algorithms. In: 2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009). IEEE, pp. 1–8
Tarchoun B, Jegham I, Khalifa AB, et al 2020 Deep CNN-based Pedestrian Detection for Intelligent Infrastructure. In: 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 1–6
Taskiran M, Kahraman N and Erdem CE 2020 Face recognition: Past, present and future (a review). Digit Signal Process 1061–28. https://doi.org/10.1016/j.dsp.2020.102809
Terven J and Cordova-Esparza D 2023 A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond. 1–27
Tian Z, Zhan R, Wang W et al (2020) Object detection in optical remote sensing images by integrating object-to-object relationships. Remote Sens Lett 11(5):416–425. https://doi.org/10.1080/2150704X.2020.1722330
Timofte R, Zimmermann K, Van Gool L (2014) Multi-view traffic sign detection, recognition, and 3D localisation. In: Machine Vision and Applications. Springer, pp 633–647
Tong K, Wu Y and Zhou F 2020 Recent advances in small object detection based on deep learning: A review. Image Vis Comput 97:103910. https://doi.org/10.1016/j.imavis.2020.103910
Tran P, Pattichis M, Celedón-Pattichis S, LópezLeiva C (2021) Facial Recognition in Collaborative Learning Videos. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, pp 252–261
Tran T-H, Nguyen DT, Phuong Nguyen T (2021) Human Posture Classification from Multiple Viewpoints and Application for Fall Detection. In: In: 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE). IEEE, pp 262–267
Triantafyllidou D, Nousi P, Tefas A (2018) Fast Deep Convolutional Face Detection in the Wild Exploiting Hard Sample Mining. Big Data Res 11(June):65–76. https://doi.org/10.1016/j.bdr.2017.06.002
Uijlings JRR, van de Sande KEA, Gevers T, Smeulders AWM (2013) Selective Search for Object Recognition. Int J Comput Vis 104(2):154–171. https://doi.org/10.1007/s11263-013-0620-5
Umer S, Rout RK, Pero C, Nappi M (2022) Facial expression recognition with trade-offs between data augmentation and deep learning features. J Ambient Intell Humaniz Comput 13(2):721–735. https://doi.org/10.1007/s12652-020-02845-8
Vandersteegen M, Van Beeck K, Goedemé T (2018) Real-Time Multispectral Pedestrian Detection with a Single-Pass Deep Neural Network. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, pp 419–426
Vashisht M, Kumar B 2020 A Survey Paper on Object Detection Methods in Image Processing. 2020 Int Conf Comput Sci Eng Appl ICCSEA 2020. https://doi.org/10.1109/ICCSEA49143.2020.9132871
Veit A, Matera T, Neumann L, et al 2016 COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
Vennelakanti A, Shreya S, Rajendran R et al (2019) Traffic Sign Detection and Recognition using a CNN Ensemble. In: In: 2019 IEEE International Conference on Consumer Electronics (ICCE). IEEE, pp 1–4
Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep Learning for Computer Vision: A Brief Review. Comput Intell Neurosci 20181–13. https://doi.org/10.1155/2018/7068349
Vuola AO, Akram SU and Kannala J 2019 Mask-RCNN and u-net ensembled for nuclei segmentation. Proc - Int Symp Biomed Imaging 2019-April (Isbi): 208–212. https://doi.org/10.1109/ISBI.2019.8759574
Wallace AM, Mukherjee S, Toh B, Ahrabian A (2021) Combining automotive radar and LiDAR for surface detection in adverse conditions. IET Radar, Sonar Navig 15(4):359–369. https://doi.org/10.1049/rsn2.12042
Wan S, Xu X, Wang T, Gu Z (2021) An Intelligent Video Analysis Method for Abnormal Event Detection in Intelligent Transportation Systems. IEEE Trans Intell Transp Syst 22(7):4487–4495. https://doi.org/10.1109/TITS.2020.3017505
Wang W 2020 Detection of panoramic vision pedestrian based on deep learning. Image Vis Comput 103:103986. https://doi.org/10.1016/j.imavis.2020.103986
Wang K and Belongie S 2010 Word Spotting in the Wild. In: 11th European Conference on Computer Vision. Springer, 591–604
Wang Q, Fu W (2018) Research on traffic sign detection algorithm based on deep learning. Concurr Comput Pract Exp 30(22):1–8. https://doi.org/10.1002/cpe.4675
Wang H, Miao F (2022) Building extraction from remote sensing images using deep residual U-Net. Eur J Remote Sens 55(1):71–85. https://doi.org/10.1080/22797254.2021.2018944
Wang J, Yuan Y and Yu G 2017 Face Attention Network: An Effective Face Detector for the Occluded Faces. 1–10
Wang S, Du Y and Huang Z 2017 Ear detection using fully convolutional networks. ACM Int Conf Proceeding Ser Part F 1319:50–55. https://doi.org/10.1145/3141166.3141168
Wang W, Shen J, Shao L (2018) Video Salient Object Detection via Fully Convolutional Networks. IEEE Trans Image Process 27(1):38–49. https://doi.org/10.1109/TIP.2017.2754941
Wang W, Shen J, Yang R, Porikli F (2018) A Unified Spatiotemporal Prior based on Geodesic Distance for Video Object Segmentation. IEEE Trans Pattern Anal Mach Intell 40(1):20–33. https://doi.org/10.1109/TPAMI.2017.2662005
Wang W, Shen J, Yang R, Porikli F (2018) Saliency-Aware Video Object Segmentation. IEEE Trans Pattern Anal Mach Intell 40(1):20–33. https://doi.org/10.1109/TPAMI.2017.2662005
Wang H, Li J, Zhou Y, et al 2019 Research on the Technology of Indoor and Outdoor Integration Robot Inspection in Substation. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2366–2369
Wang R, Xu J, Han TX (2019) Object instance detection with pruned Alexnet and extended training data. Signal Process Image Commun 70(March 2018):145–156. https://doi.org/10.1016/j.image.2018.09.013
Wang W, Xie E, Li X, et al 2019 Shape Robust Text Detection With Progressive Scale Expansion Network. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 9336–9345
Wang A, Sun Y, Kortylewski A and Yuille A 2020 Robust Object Detection Under Occlusion With Context-Aware CompositionalNets. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 12642–12651
Wang C-Y, Bochkovskiy A and Liao H-YM 2020 Scaled-YOLOv4: Scaling Cross Stage Partial Network. arXiv
Wang N, Wang Y and Er MJ 2020 Review on deep learning techniques for marine object recognition: Architectures and algorithms. Control Eng Pract (April): 104458. https://doi.org/10.1016/j.conengprac.2020.104458
Wang Q, Zhang L, Li Y and Kpalma K 2020 Overview of deep-learning based methods for salient object detection in videos. Pattern Recognit 104:107340. https://doi.org/10.1016/j.patcog.2020.107340
Wang Y, Xie H, Zha Z, et al 2020 ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 11753–11762
Wang J, Song L, Li Z, et al 2021 End-to-End Object Detection with Fully Convolutional Network. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 15844–15853
Wang C-Y, Bochkovskiy A and Liao H-YM 2022 YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. 1–15
Wang G, Ding H, Li B et al (2022) Trident-YOLO: Improving the precision and speed of mobile device object detection. IET Image Process 16(1):145–157. https://doi.org/10.1049/ipr2.12340
Wang J, Min W, Hou S et al (2022) LogoDet-3K: A Large-scale Image Dataset for Logo Detection. ACM Trans Multimed Comput Commun Appl 18(1):1–19. https://doi.org/10.1145/3466780
Wen L, Du D, Cai Z et al (2020) UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking. Comput Vis Image Underst 193(December 2019):102907. https://doi.org/10.1016/j.cviu.2020.102907
Wong F, Hu H (2019) Adaptive learning feature pyramid for object detection. IET Comput Vis 13(8):742–748. https://doi.org/10.1049/iet-cvi.2018.5654
Woźniak M, Połap D (2018) Object detection and recognition via clustered features. Neurocomputing 3201–9. https://doi.org/10.1016/j.neucom.2018.09.003
Wu C-W and Ding J-J 2021 Multi-Viewpoint Patterns and Occlusions Handling Using Hybrid Features for Vehicle Tracking. In: 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 1–5
Wu S, Zhang L (2018) Using Popular Object Detection Methods for Real Time Forest Fire Detection. In: In: 2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE, pp 280–284
Wu J, Zhou C, Zhang Q, et al 2020 Self-Mimic Learning for Small-scale Pedestrian Detection. In: Proceedings of the 28th ACM International Conference on Multimedia. ACM, New York, NY, USA, pp. 1–9
Wu K, Bai C, Wang D, et al 2021 Improved Object Detection Algorithm of YOLOv3 Remote Sensing Image. IEEE Access 9113889–113900. https://doi.org/10.1109/ACCESS.2021.3103522
Wu S, Xu Y, Zhang B, et al 2021 Deformable Template Network (DTN) for Object Detection. IEEE Trans Multimed 1–11. https://doi.org/10.1109/TMM.2021.3075323
Wu Y, Feng S, Huang X, Wu Z (2021) L4Net: An anchor-free generic object detector with attention mechanism for autonomous driving. IET Comput Vis 15(1):36–46. https://doi.org/10.1049/cvi2.12015
Wu YH, Gao SH, Mei J et al (2021) JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation. IEEE Trans Image Process 30(Xx):3113–3126. https://doi.org/10.1109/TIP.2021.3058783
Wu J, Du J, Wang F et al (2022) A multimodal attention fusion network with a dynamic vocabulary for TextVQA. Pattern Recogn 122(108214):1–10. https://doi.org/10.1016/j.patcog.2021.108214
Xia GS, Bai X, Ding J, et al 2018 DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 3974–3983. https://doi.org/10.1109/CVPR.2018.00418
Xiao Y, Tian Z, Yu J et al (2020) A review of object detection based on deep learning. Multimed Tools Appl 79(33–34):23729–23791. https://doi.org/10.1007/s11042-020-08976-6
Xiao Y, Jiang A, Ye J, Wang M-W (2020) Making of Night Vision: Object Detection Under Low-Illumination. IEEE Access 8123075–123086. https://doi.org/10.1109/ACCESS.2020.3007610
Xiao B, Lin Q, Chen Y (2021) A vision-based method for automatic tracking of construction machines at nighttime based on deep learning illumination enhancement. Autom Constr 127(March):103721. https://doi.org/10.1016/j.autcon.2021.103721
Xing J, Fang G, Zhong J and Li J 2019 Application of Face Recognition Based on CNN in Fatigue Driving Detection. In: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing - AIAM 2019. ACM Press, New York, New York, USA, 1–5
Xiong S, Liu Y, Yan Y et al (2021) Object recognition for power equipment via human-level concept learning. IET Gener Transm Distrib 15(10):1578–1587. https://doi.org/10.1049/gtd2.12088
Xu D, Ouyang W, Ricci E, et al 2017 Learning Cross-Modal Deep Representations for Robust Pedestrian Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 5363–5371
Xu S, Cheng Y, Gu K, et al 2017 Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 4743–4752
Xu X, Wang S, Wang Z et al (2021) Exploring Image Enhancement for Salient Object Detection in Low Light Images. ACM Trans Multimed Comput Commun Appl 17(1s):1–19. https://doi.org/10.1145/3414839
Xu B, Wang W, Guo L, et al 2022 CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss. Comput Electron Agric 193:106675. https://doi.org/10.1016/j.compag.2021.106675
Xu H, Guo M, Nedjah N, et al 2022 Vehicle and Pedestrian Detection Algorithm Based on Lightweight YOLOv3-Promote and Semi-Precision Acceleration. IEEE Trans Intell Transp Syst 1–12. https://doi.org/10.1109/TITS.2021.3137253
Xue C, Lu S and Hoi S 2022 Detection and rectification of arbitrary shaped scene texts by using text keypoints and links. Pattern Recognit 1241–31. https://doi.org/10.1016/j.patcog.2021.108494
Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4):611–629. https://doi.org/10.1007/s13244-018-0639-9
Yang B, Yan J, Lei Z, Li SZ 2015 Fine-grained evaluation on face detection in the wild. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). IEEE, 1–7
Yang S, Luo P, Loy CC and Tang X 2016 WIDER FACE: A Face Detection Benchmark. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 5525–5533
Yang S, Xiong Y, Loy CC and Tang X 2017 Face Detection through Scale-Friendly Deep Convolutional Networks
Yang S, Luo P, Loy CC, Tang X (2018) Faceness-Net: Face Detection through Deep Facial Part Responses. IEEE Trans Pattern Anal Mach Intell 40(8):1845–1859. https://doi.org/10.1109/TPAMI.2017.2738644
Yang T, Wu J, Liu L, et al 2020 VTD-Net: Depth Face Forgery Oriented Video Tampering Detection based on Convolutional Neural Network. In: 2020 39th Chinese Control Conference (CCC). IEEE, 7247–7251
Yang H, Liu P, Hu Y, Fu J (2021) Research on underwater object recognition based on YOLOv3. Microsyst Technol 27(4):1837–1844. https://doi.org/10.1007/s00542-019-04694-8
Yang W, Zhang J, Chen Z, Xu Z (2021) An efficient semantic segmentation method based on transfer learning from object detection. IET Image Process 15(1):57–64. https://doi.org/10.1049/ipr2.12005
Yao C, Bai X, Liu W, et al. 2012 Detecting texts of arbitrary orientations in natural images. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1083–1090
Yao L, Liu H, Hu Z, et al 2019 Cow face detection and recognition based on automatic feature extraction algorithm. ACM Int Conf Proceeding Ser. https://doi.org/10.1145/3321408.3322628
Yi J, Wu P, Metaxas DN (2019) ASSD: Attentive single shot multibox detector. Comput Vis Image Underst 189(November 2018):102827. https://doi.org/10.1016/j.cviu.2019.102827
Yu H, Zhang C, Li X, et al 2019 An End-to-End Video Text Detector with Online Tracking. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 601–606
Yu Z, Zhuge Y, Lu H and Zhang L 2019 Joint learning of saliency detection and weakly supervised semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision. IEEE, 7222–7232
Yuan L and Lu F 2018 Real-Time Ear Detection Based On Embedded Systems. In: 2018 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 115–120
Yuan J, Xiong HC, Xiao Y, et al (2020) Gated CNN: Integrating multi-scale feature layers for object detection. Pattern Recognit 105:107131. https://doi.org/10.1016/j.patcog.2019.107131
Yuan Y, Chen L, Wu H, Li L (2021) Advanced agricultural disease image recognition technologies: A review. Inf Process Agric 9(1):1–12. https://doi.org/10.1016/j.inpa.2021.01.003
Yuanchen Y, Yunfei C, Dongsheng W (2021) GridNet-3D: A Novel Real-Time 3D Object Detection Algorithm Based on Point Cloud. Chin J Electron 30(5):931–939. https://doi.org/10.1049/cje.2021.07.004
Yucel MK, Bilge YC, Oguz O, et al. 2018 Wildest Faces: Face Detection and Recognition in Violent Settings
Yuliang L, Lianwen J, Shuaitao Z and Sheng Z 2017 Detecting Curve Text in the Wild: New Dataset and New Solution
Zakria Z, Deng J, Kumar R, et al 2022 Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4. IEEE J Sel Top Appl Earth Obs Remote Sens 151039–1048. https://doi.org/10.1109/JSTARS.2022.3140776
Zhang H and Hong X 2019 Recent progresses on object detection: a brief review. In: Multimedia Tools and Applications. Multimedia Tools and Applications, pp. 27809–27847
Zhang L, Ma J (2021) Salient Object Detection Based on Progressively Supervised Learning for Remote Sensing Images. IEEE Trans Geosci Remote Sens 59(11):9682–9696. https://doi.org/10.1109/TGRS.2020.3045708
Zhang Q, Wan C and Jiang M 2017 Multiple objects detection based on improved faster RCNN. ACM Int Conf Proceeding Ser, pp. 99–103. https://doi.org/10.1145/3163080.3163101
Zhang S, Benenson R and Schiele B 2017 CityPersons: A Diverse Dataset for Pedestrian Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 4457–4465
Zhang S, Zhu X, Lei Z, et al 2017 S^3FD: Single Shot Scale-Invariant Face Detector. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, pp. 192–201
Zhang S, Wen L, Shi H et al (2019) Single-Shot Scale-Aware Network for Real-Time Face Detection. Int J Comput Vis 127(6–7):537–559. https://doi.org/10.1007/s11263-019-01159-3
Zhang W, Liu X, Yuan J, et al 2019 RCNN-based foreign object detection for securing power transmission lines (RCNN4SPTL). Procedia Comput Sci 147:331–337. https://doi.org/10.1016/j.procs.2019.01.232
Zhang J, Wu X, Hoi SCH and Zhu J 2020 Feature agglomeration networks for single stage face detection. Neurocomputing 380:180–189. https://doi.org/10.1016/j.neucom.2019.10.087
Zhang J, Xie Z, Sun J et al (2020) A Cascaded R-CNN With Multiscale Attention and Imbalanced Samples for Traffic Sign Detection. IEEE Access 8:29742–29754. https://doi.org/10.1109/ACCESS.2020.2972338
Zhang M, Liu T, Piao Y, et al 2021 Auto-MSFNet: Search Multi-scale Fusion Network for Salient Object Detection. In: Proceedings of the 29th ACM International Conference on Multimedia. ACM, New York, NY, USA, 667–676
Zhang W, Li H, Li Y et al (2021) Application of deep learning algorithms in geotechnical engineering: a short critical review. Artif Intell Rev 54(8):5633–5673. https://doi.org/10.1007/s10462-021-09967-1
Zhang X, Wang W, Zhao Y, Xie H (2021) An improved YOLOv3 model based on skipping connections and spatial pyramid pooling. Syst Sci Control Eng 9(S1):142–149. https://doi.org/10.1080/21642583.2020.1824132
Zhang X, Liu Y, Huo C, et al 2022 PSNet: Perspective-sensitive convolutional network for object detection. Neurocomputing 468:384–395. https://doi.org/10.1016/j.neucom.2021.10.068
Zhao W, Ma W, Jiao L et al (2019) Multi-Scale Image Block-Level F-CNN for Remote Sensing Images Object Detection. IEEE. Access 7(c):43607–43621. https://doi.org/10.1109/ACCESS.2019.2908016
Zhao Z-QQ, Zheng P, Xu S-TT, Wu X (2019) Object Detection With Deep Learning: A Review. IEEE Trans Neural Networks Learn Syst 30(11):3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
Zhao X, Zhang J, Tian J et al (2021) Multiscale object detection in high-resolution remote sensing images via rotation invariant deep features driven by channel attention. Int J Remote Sens 42(15):5764–5783. https://doi.org/10.1080/01431161.2021.1931537
Zhiqiang W and Jun L 2017 A review of object detection based on convolutional neural network. In: 2017 36th Chinese Control Conference (CCC). IEEE, 11104–11109
Zhong Z, Sun L and Huo Q 2019 Improved localization accuracy by LocNet for Faster R-CNN based text detection in natural scene images. Pattern Recognit 96:106986. https://doi.org/10.1016/j.patcog.2019.106986
Zhong Z, Sun L, Huo Q (2019) An anchor-free region proposal network for Faster R-CNN-based text detection approaches. Int J Doc Anal Recognit 22(3):315–327. https://doi.org/10.1007/s10032-019-00335-y
Zhou P, Han X, Morariu VI and Davis LS 2017 Two-Stream Neural Networks for Tampered Face Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 1831–1839
Zhou T, Fan DP, Cheng MM et al (2021) RGB-D salient object detection: A survey. Comput Vis Media 7(1):37–69. https://doi.org/10.1007/s41095-020-0199-z
Zhu Y and Du J 2021 TextMountain: Accurate scene text detection via instance segmentation. Pattern Recognit 110:107336. https://doi.org/10.1016/j.patcog.2020.107336
Zhu Y and Jiang Y 2020 Optimization of face recognition algorithm based on deep learning multi feature fusion driven by big data. Image Vis Comput 104:104023. https://doi.org/10.1016/j.imavis.2020.104023
Zhu H, Chen X, Dai W, et al 2015 Orientation robust object detection in aerial images using deep convolutional neural network. In: 2015 IEEE International Conference on Image Processing (ICIP). IEEE, pp. 3735–3739
Zhu Z, Liang D, Zhang S, et al 2016 Traffic-Sign Detection and Classification in the Wild. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 2110–2118
Zhu H, Zhang P, Wang L et al (2019) A multiscale object detection approach for remote sensing images based on MSE-DenseNet and the dynamic anchor assignment. Remote Sens Lett 10(10):959–967. https://doi.org/10.1080/2150704X.2019.1633486
Zitnick CL, Dollár P (2014) Edge Boxes: Locating Object Proposals from Edges. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, pp 391–405
Zou Z, Shi Z (2018) Random access memories: A new paradigm for target detection in high resolution aerial remote sensing images. IEEE Trans Image Process 27(3):1100–1111. https://doi.org/10.1109/TIP.2017.2773199
Zou Z, Shi Z, Guo Y and Ye J 2019 Object Detection in 20 Years: A Survey. 1–39
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Kaur, J., Singh, W. A systematic review of object detection from images using deep learning. Multimed Tools Appl 83, 12253–12338 (2024). https://doi.org/10.1007/s11042-023-15981-y
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DOI: https://doi.org/10.1007/s11042-023-15981-y