Multiscale Feature Learning Based on Enhanced Feature Pyramid for Vehicle Detection
Author: 
Hoanh Nguyen Academic Editor: Kai HuAuthors Info & Claims
Abstract
Vehicle detection is a crucial task in autonomous driving systems. Due to large variance of scales and heavy occlusion of vehicle in an image, this task is still a challenging problem. Recent vehicle detection methods typically exploit feature pyramid to detect vehicles at different scales. However, the drawbacks in the design prevent the multiscale features from being completely exploited. This paper introduces a feature pyramid architecture to address this problem. In the proposed architecture, an improving region proposal network is designed to generate intermediate feature maps which are then used to add more discriminative representations to feature maps generated by the backbone network, as well as improving the computational cost of the network. To generate more discriminative feature representations, this paper introduces multilayer enhancement module to reweight feature representations of feature maps generated by the backbone network to increase the discrimination of foreground objects and background regions in each feature map. In addition, an adaptive RoI pooling module is proposed to pool features from all pyramid levels for each proposal and fuse them for the detection network. Experimental results on the KITTI vehicle detection benchmark and the PASCAL VOC 2007 car dataset show that the proposed approach obtains better detection performance compared with recent methods on vehicle detection.
References
[1]
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-cnn: towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017.
[2]
W. Liu, D. Anguelov, D. Erhan et al., “SSD: single shot MultiBox detector,” in Proceedings of the European Conference on Computer Vision, pp. 21–37, Springer, Amsterdam, The Netherlands, October 2016.
[3]
J. Redmon and A. Farhadi, “YOLO9000: better, faster, stronger,” in Proceedings of the CVPR, pp. 1–9, Honolulu, HI, USA, July 2017.
[4]
T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936–944, Honolulu, HI, USA, July 2017.
[5]
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999–3007, Venice, Italy, October 2017.
[6]
P. Xiong, K. He, E. Q. Wu, L. T. E. Zhu, A. Song, and P. X. Liu, “Human exploratory procedures based hybrid measurement fusion for material recognition,” IEEE/ASME Transactions on Mechatronics, 2021.
[7]
C. Guo, B. Fan, Q. Zhang, S. Xiang, and C. Pan, “Augfpn: improving multi-scale feature learning for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12595–12604, Seattle, WA, USA, June 2020.
[8]
H. Nguyen, “Fast traffic sign detection approach based on lightweight network and multilayer proposal network,” Journal of Sensors, vol. 2020, 13 pages, 2020.
[9]
B. Chen, M. Xia, and J. Huang, “Mfanet: a multi-level feature aggregation network for semantic segmentation of land cover,” Remote Sensing, vol. 13, no. 4, p. 731, 2021.
[10]
M. Xia, Y. Cui, Y. Zhang, Y. Xu, J. Liu, and Y. Xu, “DAU-Net: a novel water areas segmentation structure for remote sensing image,” International Journal of Remote Sensing, vol. 42, no. 7, pp. 2594–2621, 2021.
[11]
M. Xia, T. Wang, Y. Zhang, J. Liu, and Y. Xu, “Cloud/shadow segmentation based on global attention feature fusion residual network for remote sensing imagery,” International Journal of Remote Sensing, vol. 42, no. 6, pp. 2022–2045, 2021.
[12]
N. C. Mithun, N. U. Rashid, and S. M. M. Rahman, “Detection and classification of vehicles from video using multiple time-spatial images,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 3, pp. 1215–1225, 2012.
[13]
A. Ottlik and H.-H. Nagel, “Initialization of model-based vehicle tracking in video sequences of inner-city intersections,” International Journal of Computer Vision, vol. 80, no. 2, pp. 211–225, 2008.
[14]
S. Kyo, T. Koga, K. Sakurai, and S. Okazaki, “A robust vehicle detecting and tracking system for wet weather conditions using the IMAP-VISION image processing board,” in Proceedings of the ITSC, pp. 423–428, Tokyo, Japan, October 1999.
[15]
J. Cui, F. Liu, Z. Li, and Z. Jia, “Vehicle localisation using a single camera,” In Proc. 2018 IEEE Intelligent Vehicles Symposium, pp. 871–876, Jun. 2010.
[16]
Q. Yuan, A. Thangali, V. Ablavsky, and S. Sclaroff, “Learning a family of detectors via multiplicative kernels,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 3, pp. 514–530, 2011.
[17]
J.-W. Hsieh, L.-C. Chen, and D.-Y. Chen, “Symmetrical SURF and its applications to vehicle detection and vehicle make and model recognition,” in Proceedings of the IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp. 6–20, February 2014.
[18]
R. M. Z. Sun and G. Bebis, “Monocular precrash vehicle detection: features and classifiers,” in Proceedings of the IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 2019–2034, September 2006.
[19]
W. C. Chih-Wei Cho and C. W. Cho, “Online boosting for vehicle detection,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 3, pp. 892–902, 2010.
[20]
W. Chu, Y. Liu, C. Shen, D. Cai, and X.-S. Hua, “Multi-task vehicle detection with region-of-interest voting,” IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 432–441, 2018.
[21]
Y. Cai, Z. Liu, X. Sun, L. Chen, H. Wang, and Y. Zhang, “Vehicle detection based on deep dual-vehicle deformable Part Models,” Journal of Sensors, vol. 2017, 10 pages, 2017.
[22]
X. Li, Y. Liu, Z. Zhao, Y. Zhang, and L. He, “A deep learning approach of vehicle multitarget detection from traffic video,” Journal of Advanced Transportation, vol. 2018, 11 pages, 2018.
[23]
H. Wang, X. Lou, Y. Cai, Y. Li, and L. Chen, “Real-time vehicle detection algorithm based on vision and lidar point cloud fusion,” Journal of Sensors, vol. 2019, 9 pages, 2019.
[24]
X. Yuan, S. Su, and H. Chen, “A graph-based vehicle proposal location and detection algorithm,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 12, pp. 3282–3289, 2017.
[25]
X. Hu, X. Xu, Y. Xiao et al., “SINet: a scale-insensitive convolutional neural network for fast vehicle detection,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 1010–1019, 2018.
[26]
H. Nguyen, “Improving Faster R-CNN framework for fast vehicle detection,” Mathematical Problems in Engineering, vol. 2019, 11 pages, 2019.
[27]
A. G. Howard, M. Zhu, B. Chen et al., “MobileNets: efficient convolutional neural networks for mobile vision applications,” Clinical Orthopaedics and Related Research, 2017, https://arxiv.org/abs/1704.04861.
[28]
S. Mehta, M. Rastegari, L. Shapiro, and H. Hajishirzi, “Espnetv2: a light-weight, power efficient, and general purpose convolutional neural network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9190–9200, Long Beach, CA, USA, June 2019.
[29]
S. Liu, Q. Lu, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768, Salt Lake City, UT, USA, June 2018.
[30]
M. Tan, R. Pang, and V. Quoc, “Efficientdet: scalable and efficient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790, Seattle, WA, USA, June 2020.
[31]
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969, Honolulu, HI, USA, July 2017.
[32]
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, Las Vegas, NV, USA, June 2016.
[33]
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in Proceedings of the CVPR, pp. 3354–3361, Providence, RI, USA, June 2012.
[34]
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International Journal of Computer Vision, vol. 88, no. 2, pp. 303–338, 2010.
[35]
Y. Xiang, W. Choi, Y. Lin, and S. Savarese, “Subcategory-aware convolutional neural networks for object proposals and detection,” in Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 924–933, IEEE, Santa Rosa, CA, USA, March 2017.
[36]
K. Chen, J. Pang, J. Wang et al., Chen Change Loy, and Dahua Lin. Mmdetection, 2018, https://github.com/open-mmlab/mmdetection.
[37]
Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos, “A unified multi-scale deep convolutional neural network for fast object detection,” in Proceedings of the Computer Vision - ECCV 2016, pp. 354–370, Amsterdam, The Netherlands, October 2016.
[38]
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448, Santiago, Chile, December 2015.
[39]
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627–1645, 2009.
[40]
J. Redmon and A. Farhadi, “Yolov3: an incremental improvement,” 2018, https://arxiv.org/abs/1804.02767.
Recommendations
Smoky vehicle detection based on multi-feature fusion and ensemble neural networks
Existing methods of smoky vehicle detection from the traffic flow are inefficiency and need a large number of workers. To solve this issue, we propose an automatic smoky vehicle detection method based on multi-feature fusion and ensemble back-...
Latent Feature Pyramid Network for Object Detection
Object detection methods based on Convolution Neural Networks (CNN) usually utilize feature pyramid networks to detect objects with various scales. The state-of-the-art feature pyramid networks improve detection accuracy by enhancing multi-level feature ...
Vehicle object counting network based on feature pyramid split attention mechanism
AbstractIn recent years, real-time vehicle congestion detection has become a hot research topic in the field of transportation due to the frequent occurrence of highway traffic jams. Vehicle congestion detection generally adopts a vehicle counting ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2021 Hoanh Nguyen.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
John Wiley & Sons, Inc.
United States
Publication History
Published: 01 January 2021
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 24 Jan 2025