Road Damage Detection with Models Learning from Each Other

Fangjun Wang, Jianing Wei, Nan Zhang, Yasuto Watanabe, Shunichi Watanabe, Genta Suzuki, Zhiming Tan

Abstract: Road damage detection is an important part of road maintenance and management, as it allows for timely repairs and helps to extend the lifespan of roads. Recent advancements in deep learning have led to the development of deep models that can analyze images from road inspections to automatically detect and classify damage. In this work, we propose a novel three-stage learning approach for road damage detection. Our method combines multiple state-of-the-art object detection models and leverage their strengths through mutual learning and knowledge distillation. The approach achieves an Fl-score of 0.7927 and an inference speed of 0.0268 seconds per image, securing the first place in both Phase 1 and Phase 2 of the Optimized Road Damage Detection Challenge (ORDDC’2024). The extensive experiments and comparisons with existing methods demonstrate the effectiveness of our method.