Utilize Data Augmentation and Flexi Corner Block for Road Damage Detection - Zhaohui Wu; Runjing Zhao; Xingliang Sun; Zhaojia Li (IEEE Access)
Road damage detection is crucial for ensuring road safety and maintaining infrastructure durability, especially as increasing traffic and aging road networks create growing challenges for transportation systems worldwide. This process typically involves detecting irregularities on the road surface, such as cracks and potholes, from images. Current methods, integrating Single Shot Detector with MobileNet and Faster R-CNN, have successfully detected large potholes and long, deep cracks. However, detecting smaller, shallower cracks and minor potholes remains a challenge. Additionally, the appearance of road damage can vary under different weather conditions, making consistent detection difficult. To address these challenges, three modules are proposed. The Weather Trim Augment module leverages stable diffusion to generate weather-specific road damage datasets by adjusting prompt words and parameters, ensuring crack location and type remain unchanged, thus improving detection under various weather conditions. The Flexi Corner Block module enhances road damage detection by combining deformable convolutions with lightweight MLP and Learnable Local Attention, improving feature learning in corner areas. Moreover, the HXIOU loss function uses weighted calculations to effectively mine difficult samples, enhancing detection capabilities for challenging damages such as blurry potholes and fine cracks. Experimental results on the RDD2020 and CNRDD datasets demonstrate improved performance. On one hand, the proposed method achieves 64.9 on Test1 and a 40.6 F1-Score, demonstrating strong detection capabilities. On the other hand, the model exhibits robust generalization under adverse weather conditions, such as rain and snow, further validating its effectiveness in diverse environments.