RS-YOLO: An Optimized YOLOv8 Framework for Mining Area Traffic Safety Monitoring

The driving conditions in mining regions are intricate, marked by limited visibility and significant disturbances. Current object detection models often encounter high rates of missed detections and false positives when applied to monitoring driver behavior and road conditions for safety. To tackle these challenges, we introduce RS-YOLO (Residual CSP YOLO), an enhanced YOLO framework. Firstly, we develop a novel lightweight feature extraction module that employs expanded feature maps and multi-layer convolutions within an inverted residual architecture. This approach enhances feature extraction efficiency and reduces both missed and false detections. Secondly, we incorporate a pre-activation mechanism to optimize the network, which improves gradient flow, ensures training stability, and accelerates convergence speed. Finally, we integrate the PIOU loss function to more accurately address the alignment errors between predicted and ground truth bounding boxes in complex scenarios. Experimental results demonstrate that RS-YOLO achieves relative improvements of 3.59% compared to YOLOv8n and 1.63% over YOLOv8s on the mining dataset, while also reducing the number of parameters and computational complexity by 50% relative to YOLOv8s. Additionally, on the COCO dataset, RS-YOLO achieves a 27% accuracy improvement over YOLOv8n. These outcomes exhibit RS-YOLO's superior detection capabilities, lower computational demands, and strong generalization performance in challenging environments.