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.

With the growth of the city and the increase in the number of cars, the maintenance and management of roads attract more attention. Road damage detection of road images is the basic step of road maintenance. To reduce the cost of labor, it is crucial to make the best use of road damage images from different geographical environments and capturing devices. This paper describes our 1-st place solution used in the Crowd sensing-based Road Damage Detection Challenge of the 2022 IEEE International Conference on Big Data. We use YOLO-series models and Faster RCNN as our one-stage and two-stage baseline models respectively. Our model only needs to be trained directly on the datasets of the overall six countries. Besides, with ensemble learning and test time augmentation, our ensemble model achieves the best results on the learderboard of each single country (India, Japan, United States, and Norway) without fine-tuning. Our ensemble model achieves the F1 scores of 0.7699 and 0.7160 on Overall and Average leaderboard, which significantly outperformed the 2-nd p lace F1 s cores of 0.7432 and 0.6744. The source code and trained model are available at https://github.com/berry-ding/ShiYu_SeaView_GRDDC2022.