YOLOv8 for adverse weather: traffic sign detection in autonomous driving
Boxiang He, Yiming Yang, Sitao Zheng, Guangyang Fan · 2024
This paper presents an enhanced YOLOv8 model incorporating Squeeze-and-Excitation (SE) Attention and Sub-Pixel Convolution aimed at improving traffic sign detection under various environmental conditions. Through rigorous evaluation on the CCTSDB2021 dataset, our model demonstrates superior performance over the standard YOLOv8 and YOLOv7 models, particularly in challenging low-light scenarios. The introduction of SE-Attention allows for refined feature recalibration, while Sub-Pixel Convolution effectively increases resolution, enhancing the detection accuracy. The findings confirm that our architectural enhancements significantly boost the precision, recall, and F1-score, thereby enhancing the model’s robustness and reliability. This study sets a new benchmark for traffic sign detection systems and proposes directions for future research to further advance the field.