An improved fighting behavior recognition algorithm based on YOLOv8:EFD-YOLO
In today's society,fighting behavior detection technology is crucial for preventing violent incidents and conflicts.By integrating surveillance cameras with object detection,real-time monitoring of crowd activities becomes possible,effectively preempting potential threats.Based on YOLOv8,EFD-YOLO employs EfficientRep to replace the backbone network,enhancing the efficiency of feature ex-traction and enabling accurate real-time feature extraction within the surveillance area.The introduction of the FocalNeXt focus module,through a combination of deep convolutions and skip connections,ad-dresses occlusion issues and multi-scale feature requirements.Furthermore,Focal-DIoU is adopted as the bounding box regression loss function,reducing false detections in complex scenarios.Experimental results show that the EFD-YOLO algorithm outperforms YOLOv8n by 4.2％in the mAP@0.5 metric and 2.5％in the mAP@0.5∶0.95 metric,making it suitable for real-time detection of fighting behaviors in critical locations.
万方数据
维普
