计算机工程与应用2024，Vol.60Issue(11)：105-114,10.DOI:10.3778/j.issn.1002-8331.2307-0190
改进YOLOv5的高精度跌倒检测算法
High-Precision Fall Detection Algorithm with Improved YOLOv5
摘要
Abstract
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.关键词
YOLOv5/跌倒检测/C2D/Swin Transformer block/解耦结构Key words
YOLOv5/fall detection/C2D/Swin Transformer block/decoupled structure分类
信息技术与安全科学引用本文复制引用
朱胜豪,钱承山,阚希..改进YOLOv5的高精度跌倒检测算法[J].计算机工程与应用,2024,60(11):105-114,10.基金项目
国家自然科学基金青年基金项目(42105143) （42105143）
江苏省高等学校基础学科(自然科学)研究面上项目(580221016) （自然科学）
江苏省研究生实践创新计划(SJCX23_0397). （SJCX23_0397）
