A Robust Vision-Based Framework for Traffic Sign and Light Detection in Automated Driving Systems

Reliable detection of traffic signs and lights (TSLs) at long range and under varying illumination is essential for improving the perception and safety of autonomous driving systems (ADS). Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions. To overcome these limitations, this research presents FED-YOLOv10s, an improved and lightweight object detection framework based on You Only look Once v10 (YOLOv10). The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations, an Efficient Multiscale Attention (EMA) mechanism to improve TSL-invariant feature extraction, and a deformable Convolution Networks v4 (DCNv4) module to enhance multiscale spatial adaptability. Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy, attaining an F1-score of 91.8%, and mAP@0.5 of 95.1%, while reducing parameters to 8.13 million. Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision, recall, and mAP. These results highlight FED-YOLOv10s as a robust, efficient, and deployable solution for intelligent traffic perception in ADS.