A Vision-Transformer-Based Convex Variational Network for Bridge Pavement Defect Segmentation

This study addresses the fine-grained segmentation of defects in bridge pavements, which is crucial for the maintenance and structural safety of bridges. Although bridge pavements pose distinctive challenges owing to their unique characteristics and varied defect types, previous studies have primarily focused on the detection of slender cracks. To fill this research gap, we developed a novel end-to-end hybrid method that dynamically combines the vision transformer (ViT) and level set theory to handle the complex geometry of bridge pavement defects. The novelty of the proposed method lies in the configuration of two parallel decoders. These decoders, operating under a unified objective function, share weights and perform simultaneous optimization, thereby facilitating a holistic end-to-end training process. Furthermore, we compiled two new bridge pavement defect datasets, namely BdridgeDefX and BdridgeDef20, which offer broader applicability for practical defect detection. The results of a rigorous experimental validation on four datasets demonstrated the proposed method's capability of generating accurate defect boundaries and delivering state-of-the-art performance.