Research on Improved YOLOV5 Algorithm for Dense Pedestrian Detection
A feature fusion algorithm FPCA-YOLOV5 with improved YOLOV5 is proposed to address the issues of missed detection of dense pedestrians and low detection accuracy.Firstly,by combining the spatial pooling pyramid structure SPPFCSPC with CA attention,the model has stronger expressive power.Secondly,adding PP modules to the network and changing the detection layer from three to four layers can achieve more accurate detection of small targets.Finally,a novel downsampling mechanism,CAConv,was designed to enable the network to focus more on important channels when processing feature maps.The experimental results show that on the public dataset WiderPerson,the improved YOLOV5 model has increased recall by 3.4%and average accuracy by 2.3%compared to the original model.The overall perfor-mance is significantly improved compared to the original model,demonstrating the effectiveness of the FPCA-YOLOV5 algorithm in object de-tection.
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