復雜場景下基于改進的Y0L0v5-pose的異常行為檢測研究
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中圖分類號：TP391.4 文獻標識碼：A 文章編號：2096-4706（2025）07-0071-06
Abstract： This paper proposes an abnormal behavior detection algorithm based on the improved YOLOv5-pose in complexscenes.ItusesFPTtoreplacetheFPN+PANmodule，enablingthefeaturemapstoachieve globalandlocalinteraction acrossscales and spaces，and improving the accuracyofjoint point detection.Inthe Neck module，askipconnection structure is employedto efectively fusethe information ofthe input featuresand the multi-scale features output through the network， improvingtheabilitytocapturedetailed informationandenancing theauracyofdetectingoluded jointpoints.Experimental results show that the improved algorithm achieves an average accuracy of 9 9 . 5 % on the CrowdPose dataset，which is 2 . 4 % higher thanthatoftheoriginalmodel.Theimprovedmodelnotonlyhashigherdetectionaccuracybutalsosignificantlyimprovesthe recognition performance of small targets.
Keywords： YOLOv5-pose; behavior recognition; joint point detection; FPT; skip connection
0 引言
在城市人員復雜、社會安全風險高的重點場所，極易發(fā)生群體沖突、極端暴力等社會安全突發(fā)事件，加劇民眾的心理恐慌，嚴重影響到社會穩(wěn)定。（剩余8613字）