A Set of Effective Strategies for Optimized Road Damage Detection

In this paper, we propose an optimized method for road damage detection using a lightweight YOLO model as the baseline. Our approach incorporates a set of effective strategies, including lightweight attention mechanisms, data augmentation, dynamic sampling, weight averaging, and multi-step knowledge distillation. Our method significantly improves inference speed while maintaining high accuracy compared to previous mainstream ensemble-based methods. Our approach achieves notable success in the IEEE Big Data 2024 Optimized Road Damage Detection Challenge (ORDDC’2024), securing second place with an F1 score of 0.7013 and an inference time of 0.0328 s per image. These results demonstrate a strong balance between accuracy and efficiency. Extensive experiments confirm that our method boosts detection accuracy and greatly accelerates inference, making it highly suitable for real-world applications. The source code and trained model are available at https://github.com/YinglongDu/ShiYu_Kunchuan_ORDDC2024.