RFAM-YOLO: Reinforced Feature Aggregation and Multi-Attention YOLOv8s for High-Precision Traffic Sign Detection

In the field of intelligent driving, including autonomous and assisted driving, traffic sign detection is critical, as its performance directly affects vehicle safety. To address the challenges posed by complex backgrounds and the small size of traffic signs, this paper proposes an improved small-object traffic sign detection algorithm based on YOLOv8s, named RFAM-YOLO. First, since the shared parameter mechanism in standard convolution kernels can limit performance, RFCAConv and C2fRFCAConv are integrated into the backbone network to enhance convolution operations, thereby improving multi-scale feature extraction. Second, a C2fMLCA attention module is incorporated into the Neck component to enhance the representation of key object features while maintaining computational efficiency, thereby improving feature fusion. Third, to enhance the detection of multiple small traffic signs, an additional small-object detection head module is introduced to better capture their characteristics and contextual information. Finally, to account for differences in aspect ratios between predicted and ground truth bounding boxes, the original CIoU loss function is replaced with Focaler-ShapeIoU, improving bounding box regression accuracy and detection robustness. Experiments on two benchmark traffic sign datasets, TT100K and CCTSDB2021, demonstrate that RFAM-YOLO consistently improves detection performance over the YOLOv8s baseline while maintaining a lightweight model size and real-time inference speed. On the TT100K dataset, RFAM-YOLO achieves a 4. 38 percentage point increase in mean Average Precision (mAP@50), from 86. 09% to 90. 47%, with simultaneous gains in precision and recall. On the CCTSDB2021 dataset, it further boosts mAP@50 from 96. 54% to 98. 20%. These results show that the proposed architecture provides a favorable accuracy-efficiency trade-off and improved robustness to small and cluttered traffic signs in realistic driving scenarios.