Optimization of YOLOv8 Traffic Sign Object Detection Based on BiFPN Feature Pyramid and CBAM Attention Module

This study proposes the integration of BiFPN feature pyramid and CBAM attention module in YOLOv8 to enhance the robustness of traffic sign and signal detection, based on the urgent need for urban road safety and autonomous driving. The experiment was validated on a test set of 801 images and 944 targets, and the overall precision of the model reached 0.739, Recall 0.654mAP50 0.723mAP50-95 0.631 Significantly better than the baseline, with improvements of 5.2%, 1%, 2.8%, and 2.15% respectively, confirming that the improvement strategy effectively reduces false positives and improves localization classification consistency. The subdivision results show that the Stop logo achieves almost zero missed detections due to its high contrast and regular shape, with Precision and mAP50 both approaching 1; The mAP50 of the three speed limits of 20, 60, and 70 km/h all exceeded 0.82 under sufficient sample conditions, and remained above 0.75 on the stricter mAP50-95 index, indicating good generalization to scale and lighting changes; Although data is scarce for speed limits of 100 and 120 km/h, mAP50 still reaches 0.77 and 0.85, indicating that the network has fully learned the common features of circular speed limit signs; In contrast, signal lights such as Red Light have a small scale and complex background, with mAP50-95 less than 0.35 and low recall, making them a key focus for future optimization. Overall, the current model has matured for high contrast and regular shape signs. The next step should be to focus on improving the recall rate of small sample categories and traffic lights through difficult case mining, multi-scale training, and targeted data augmentation. The gap between mAP50 and mAP50-95 should be narrowed at higher IoU thresholds to meet the high reliability requirements of real road scenes. This study not only validates the effectiveness of BiFPN+CBAM in traffic sign detection, but also provides a reference for improving low sample category and small object detection, which has positive significance for promoting the safe implementation of intelligent transportation systems and autonomous driving.