改进YOLOv5的高精度跌倒检测算法OA北大核心CSTPCD

In order to counter the limitations of the original YOLOv5 human fall detection task,a highly accurate fall detection algorithm,called C2D-YOLO,is proposed in this paper.The original task struggles to effectively handle com-plex detail capture,deformation handling,target adaptation to different scales,and occlusion detection.To overcome these challenges,several improvements are made to the YOLOv5 model.Firstly,a new feature extraction module called C2D is introduced,which improves feature characterisation,captures complex details,and handles deformations by combining deformable convolution,standard convolution,and channel-space hybrid attention mechanisms.Secondly,in the neck net-work,Swin Transformer block replaces the bottleneck layer of the C3 module to retain more feature information,thereby improving target detection accuracy at different scales and enhancing performance under occlusion.Finally,the head mod-ule of YOLOv5 is enhanced based on the decoupled structure of YOLOX borrowed from YOLOv5 to optimise classifica-tion and regression performance.Experimental results show that this method achieves a 3.2 percentage points improve-ment in mAP0.5 and a 6.5 percentage points improvement in mAP0.5:0.95 compared to existing YOLOv5s.These improvements significantly increase detection accuracy and reduce false alarm rates.