基于YOLOv8改进的打架斗殴行为识别算法:EFD-YOLO

An improved fighting behavior recognition algorithm based on YOLOv8:EFD-YOLO

在当今社会,打架斗殴检测技术对于防范暴力事件和冲突至关重要。结合监控摄像头和目标检测,能够实时监测人群活动,从而有效预防潜在威胁。因此,提出了一种基于YOLOv8改进的打架斗殴行为识别算法EFD-YOLO。EFD-YOLO采用EfficientRep替换主干网络,提高了特征提取的效率,并在监控范围内实现准确实时的特征提取。引入FocalNeXt焦点模块,通过深度卷积和跳跃连接的结合,解决了遮挡问题和多尺度特征需求问题。采用Focal-DIoU作为边界框回归损失函数,在复杂情况下减少了误检的问题。实验结果显示,EFD-YOLO算法相较于YOLOv8n在mAP@0.5指标上提升了4.2%,在mAP@0.5:0.95指标上提升了2.5%,满足关键场所中实时检测打架斗殴行为的需求。

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,EFDYOLO employs EfficientRep to replace the backbone network,enhancing the efficiency of feature extraction 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,addresses 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.