Electric Bicycle Illegal Behavior Detection Based on Improved YOLOv7
To address the issues of high electric vehicle density and difficulty in violation detection and location due to overlapping,the YOLOv7-CSA-DSIoU algorithm,which integrates an attention mechanism and optimizes position loss function,is constructed.The network's ability for feature extraction is strengthened by dividing channel dimension into multiple sub-features,each sub-feature is decomposed into two parallel one-dimensional feature encoding modules for capturing the long-range dependence of spatial direction and location information.In addition,the location regression loss function based on the Scylla IoU is improved by adding the measures of vertex distance and inter-center distance between the prediction frame and the target frame to achieve the point-edge double fast convergence and improve the localization ability of target detection.The experimental results indicate that YOLOv7-CSA-DSIoU algorithm outperforms the baseline model,mAP@0.5 improved by 6.4%,mAP@0.75 improved by 4.2%,and mAP@0.5:0.95 improved by 4.3%.
electric bicycleillegal behariorYOLOIoUattention mechanism
NETL
NSTL
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