A study on front vehicle collision warning method based on lightweight YOLOv8 and DeepSort

With the continuous increase in vehicle ownership, the frequency of traffic accidents has risen significantly, and higher demands have consequently been placed on active vehicle safety technologies. To address the challenges of insufficient real-time performance and high model complexity in traditional object detection methods under complex traffic conditions, an improved front-vehicle collision warning system has been proposed by integrating YOLOv8 and DeepSort. In this approach, the original YOLOv8 backbone network is replaced by the lightweight MobileNet V4, and the Convolutional Block Attention Module (CBAM) is incorporated to enhance feature extraction capabilities. A comprehensive algorithmic framework has been constructed, integrating multi-object recognition, front-vehicle distance estimation, ego-vehicle speed calculation, and hierarchical warning level output. Experimental results on the KITTI dataset have demonstrated a detection accuracy of 95.5 % and a total detection time of 2.6 ms per frame. Additionally, a 2.6 % improvement in mAP50–95 has been observed, accompanied by only a 0.1 % decrease in the recall rate. These findings suggest that the proposed method provides effective technical support for front-vehicle collision warning in intelligent transportation environments.