Abstract: To reduce the issues of false detection and missed detection in target detection algorithms within urban environments,the YOLOv8 target detection model is used as the foundation,and a small-object detection layer is introduced to enable the network to better capture and recognize small-sized objects in the field of view,thereby improving its focus on target recognition.A novel remote sensing target detection model is integrated to reconstruct the C2f module,enhancing its perception of rich gradient flow information and its ability to dynamically adjust the receptive field.By applying topological optimization concepts to improve the CBAM attention mechanism,the GSAM attention mechanism is proposed and embedded at appropriate positions in the network to enhance the utilization of semantic information.To address the problem of missed detections,the performance of multiple IoU methods is compared,and the optimal EIoU is selected to accelerate the convergence speed of the algorithm and improve regression accuracy.Testing and ablation experiments conducted on the Cityscapes public dataset show that,compared to the baseline algorithm,the improved algorithm achieves increases of 2.5,5.8,and 6.1 percentage points in precision,recall,and mean average precision(mAP),respectively.These results effectively enhance the accuracy of vehicle target detection in urban traffic scenarios,providing reliable support for applications such as road video surveillance.

Key words: Urban traffic, Target recognition, YOLOv8, Attention mechanism, IOU