Deep Learning-Based Surface Crack Detection in Bridge Structures: A Review

Surface cracks pose a significant threat to the durability and operational safety of concrete bridges, making accurate crack detection essential for effective structural health monitoring. Conventional manual inspection is limited by low efficiency, subjective judgment, and safety risks during field operations. Although deep learning computer vision techniques have become the dominant approach for automated crack detection, existing review studies provide limited discussion of their adaptability to different bridge structures and offer insufficient guidance for practical engineering applications. This review systematically summarizes recent advances in deep learning methods for surface crack detection in concrete bridges. Existing approaches are classified into crack classification, object detection, and semantic segmentation. The review further examines the relationships among bridge geometry, crack morphology, inspection conditions, and model performance. It also compares the technical challenges associated with beam, arch, cable stayed, and suspension bridges and discusses suitable model adaptation strategies for different structural characteristics. The analysis shows that bridge specific model architectures can significantly improve detection accuracy and robustness under complex inspection conditions. However, several challenges remain, including the limited availability of high quality datasets, data imbalance, inadequate detection of small cracks on curved surfaces under multiple viewing conditions, and the high cost of field deployment. This review provides practical guidance for selecting appropriate deep learning techniques for concrete bridge inspection and highlights future research directions toward integrating advanced sensing, multimodal data fusion, and intelligent inspection technologies to achieve more accurate, efficient, and reliable bridge condition assessment.