Fine-grained detection method for illegal shared bicycles in complex scenes
Detecting illegal shared bicycles has great application value in maintaining the appearance of cities. Affected by shadow occlusion,pose difference and target overlap,the existing algorithms have the problems of false extraction,missed detection and inaccurate positioning. This paper proposes a Bicycle-YOLO algorithm for fine-grained detection of illegal shared bicycles. In view of the situation that the target is occluded by shadows,a C3_DCN module with adaptive receptive field is constructed to enhance the model's ability to identify and describe shared bicycles,and alleviate the false extraction of the model' s fine-grained detection of illegal shared bicycles. According to the parking posture difference of illegal shared bicycles,the context aggregation block is introduced to improve the accuracy of the model for multi-granularity target detection and reduce missed detection. According to Shared cycling overlap pile up crime phenomenon,WIOUv3 loss function is used to solve the problem of mixed fine-grained features of overlapping targets and accurately locate the target position. On homemade violations Shared cycling data set,we do experiments on other methods,the results show that the Bicycle-YOLO algorithm precision ratio and recall ratio,map@0. 5 with F1 were 93. 4％,87. 3％,91. 2％ and 90. 25％,obviously superior to other methods,the feasibility of the method was verified.
shared bike detectionYOLOv5deformable convolutioncontext aggregate blockloss function
万方数据
维普
