Duan et al., 2022 - Google Patents
An anchor box setting technique based on differences between categories for object detectionDuan et al., 2022
- Document ID
- 7685730826967772356
- Author
- Duan S
- Lu N
- Lyu Z
- Liu G
- Cao B
- Publication year
- Publication venue
- International Journal of Intelligent Robotics and Applications
External Links
Snippet
Detection accuracy and speed are crucial in object detection in computer vision. This work proposes a novel technique called On-Category Anchors (OC-Anchors) to improve the accuracy of real-time single-stage object detectors. The key concept of the OC-Anchors …
- 238000001514 detection method 0 title abstract description 93
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gao et al. | BLNN: Multiscale Feature Fusion‐Based Bilinear Fine‐Grained Convolutional Neural Network for Image Classification of Wood Knot Defects | |
EP4443396A1 (en) | Image encoder training method and apparatus, device, and medium | |
Duan et al. | An anchor box setting technique based on differences between categories for object detection | |
Lyu et al. | Probabilistic object detection via deep ensembles | |
Guan et al. | Multi-scale object detection with feature fusion and region objectness network | |
Zhang et al. | A new deep spatial transformer convolutional neural network for image saliency detection | |
Setyono et al. | Betawi traditional food image detection using ResNet and DenseNet | |
Zhang et al. | Research on improved YOLOv8 algorithm for insulator defect detection | |
CN112966553A (en) | Strong coupling target tracking method, device, medium and equipment based on twin network | |
Yang et al. | Real-Time object detector based MobileNetV3 for UAV applications | |
Li et al. | A UAV detection algorithm combined with lightweight network | |
Li et al. | SCD-YOLO: a lightweight vehicle target detection method based on improved YOLOv5n | |
Wang et al. | Speed-up Single Shot Detector on GPU with CUDA | |
Yang et al. | MSF-YOLO: A multi-scale features fusion-based method for small object detection | |
Li et al. | A novel algorithm for HRRP target recognition based on CNN | |
Wang et al. | Balanced-RetinaNet: solving the imbalanced problems in object detection | |
Wang et al. | A detection method for impact point water columns based on improved YOLO X | |
Liu et al. | An improved method for small target recognition based on faster RCNN | |
Xia et al. | Surface Defect Detection Using U-net and transfer learning | |
Wang et al. | Real-time dangerous objects detection in millimeter wave images | |
Chen et al. | A Method for Imbalanced Fault Diagnosis Based on Self-attention Generative Adversarial Network | |
Chen | Object detection algorithm based on lightweight convolutional neural networks for mobile devices | |
Li et al. | YOLOv3 target detection algorithm based on channel attention mechanism | |
Cheng et al. | Tire defect detection algorithm based on multi-task learning and normal feature fusion | |
Zhang et al. | Aerial infrared target tracking method based on KCF for frequency-domain scale estimation |