A traffic signal recognition method based on improved YOLOv8

In recent years, the rapid advancement of automotive and computer vision technologies has made self-driving cars a focal point of interest. A critical factor in the safe and efficient operation of self-driving cars is their ability to accurately recognize traffic signs. Consequently, traffic sign recognition has become essential for autonomous driving systems. This study introduces an optimized YOLOv8 method to enhance traffic sign recognition (TSR) performance. Recognizing that most targets are small and pose a challenge for model accuracy, we employ three different data augmentation techniques on the input images. Additionally, we improve the YOLOv8 loss function by incorporating the Wasserstein distance, which enhances the model's efficiency in detecting small targets. To validate the proposed method's effectiveness, we conducted comparative and ablation experiments. Experimental evaluations on the TT100K dataset indicate that the mAP and precision improved by 1.3% and 0.90%, compared to the standard YOLOv8. These results confirm the proposed method's superior performance in traffic sign detection.