Contrastive decoupling global and local features for pavement crack detection.

Pavement crack detection is an essential defect inspection task to ensure traffic and driving safety. Despite the recent studies that have achieved promising performance for inspecting pavement cracks, they still suffer from the challenges of background complexity, crack diversity, and generalization ability. To address these problems, we propose a contrastive decoupling network (CDNet) for detecting pavement cracks. Specifically, this contrastive decoupling framework separately extracts the global and local features with contrastive learning. It can effectively boost the discriminative power and generalization ability of the feature representations. Moreover, we propose a global semantic enhancement (GSE) module to enhance the semantic information of the global features. This module can reinforce the distinguishability for accurately identifying cracks and backgrounds. Furthermore, we propose a local detail refinement (LDR) module to refine the detailed information of the local features. This module can strengthen the localizability for detecting cracks with precise shapes. In addition, we propose a dynamic dependency-aware feature aggregation (DDFA) scheme to adaptively integrate the global and local features based on contextual dependencies. This scheme can enrich the output features for effectively detecting cracks in each image. Experimental results on four pavement crack detection datasets, namely Crack500, CrackTree200, CFD, and AEL, demonstrate that our proposed method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]