Abstract: 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 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.

Key words: object detection, fighting；YOLOv8；EfficientRep；FocalNeXt；Focal-DIoU