Cross-Scale Overlapping Patch-Based Attention Network for Road Crack Detection

Cracks on road surfaces pose serious risks to both pedestrians and drivers. Traditional manual crack detection methods are not only slow but also pose safety risks. Automating this process has the potential to greatly enhance detection efficiency and consequently improve driving safety. Although previous methods have shown promise in road crack detection, they often neglect interactions between multiple scales, causing smaller cracks to be overlooked in later stages of detection. This paper introduces the Cross-scale Overlapping Patch-based attention Network (COP-Net), which incorporates two critical components: the Scale-aware Channel Attention (SCA) module and the Patch-based Cross-scale Attention (PCA) module for crack detection. These innovations enable dynamic inference on multiple scales, resulting in a significant improvement in crack detection and segmentation. Notably, our approach excels at detecting both small and large cracks simultaneously. To validate the effectiveness of our approach, we conducted evaluations on three open datasets: CRACK500, CFD, and AEL. These evaluation results demonstrate that COP-Net surpasses eleven comparison methods, including HED, DeepCrack, UHDN, SSGNet, MFANet, FPHBN, DeepCrack, PBNet, PAFNet, CarNet, and SegFormer. Our model achieves new State-of-The-Art (SoTA) performance levels in terms of segmentation metrics such as AIU, ODS, and OIS.