Traffic sign recognition based on improved YOLOv8 algorithm in severe weather

The objective of this research is to develop an improved traffic sign recognition algorithm that can operate effectively in a variety of weather conditions. The existing algorithms have been found to have two main limitations: a slow detection speed and an insufficient detection accuracy. This study proposes an enhanced traffic sign recognition algorithm based on YOLOv8 as a solution to these issues. The experimental comparison revealed that YOLOv8 is the optimal base model for this purpose. The ADown module is employed to supplant the downsampling structure in YOLOv8, thereby optimising the YOLOv8 detection head framework with shared volumes. This approach results in a notable reduction in algorithmic parameters while maintaining accuracy, thereby enhancing the detection speed. The results of comparative experiments demonstrate that the improved algorithm exhibits an average accuracy mAP50 of up to 94.0% when utilising the same dataset. This value is higher than that of other algorithms, and the predicted accuracy is enhanced under varying weather and night conditions. Furthermore, the leakage rate is significantly reduced, aligning with the requirements of the road traffic sign recognition task.