Improved Vehicle Object Detection Algorithm Based on Swin-YOLOv5s

In response to the challenges of low detection accuracy, slow speed, and high rates of false positives and missed detections in existing YOLOv5s vehicle detection models under complex traffic scenarios, an improved Swin-YOLOv5s vehicle detection algorithm is proposed in this paper. By incorporating the Swin Transformer attention mechanism to replace the original C3-1 network, the computational load is reduced and the capability of capturing global features is enhanced. The Self-Concat feature fusion method is enhanced to enable adaptive adjustment of the feature map weights, thereby enhancing positive features. The results of experiments conducted on the KITTI dataset and tests with the Tesla V100 indicate that the proposed improved Swin-YOLOv5s algorithm achieves a mean average precision (mAP) of 95.7% and an F1 score of 93.01%. These metrics represent improvements of 1.6% and 0.56%, respectively, compared to YOLOv5s. Additionally, the inference speed for a single image increases by 1.11%, while the overall detection speed in frames per second (FPS) improves by 12.5%. This enhancement effectively addresses issues related to false positives and missed detections encountered by YOLOv5s under severe vehicle occlusion conditions. The ablation experiments and comparative experiments with different network models validate both the efficiency and accuracy of this model, demonstrating its enhanced capability to meet practical vehicle detection requirements more effectively.