A Global-Local Fusion Model via Edge Enhancement and Transformer for Pavement Crack Defect Segmentation

Pavement crack defect detection is an important task in road maintenance. Accurate detection of crack defects has far-reaching significance in maintaining the health condition of roads. Although many excellent crack defect detection algorithms have emerged, the detection effect on the edge details of the crack defects is still not ideal. In this paper, we propose a novel global-local fusion network based on edge enhancement and Transformer for pavement crack defect segmentation. Aiming at the structural characteristics of the pavement crack defects, combined with the edge detection algorithm (Sobel), an edge feature enhancement (EFE) module is presented to realize the accurate extraction of local detail information of the pavement crack defects. Meanwhile, a Transformer-based encoding path is also built to extract rich global information. Faced with the two different types of feature information, an adaptive fusion (AF) module is proposed to realize the efficient fusion of the two types of feature information. Furthermore, an attention-based local feature enhancement (ALFE) module and an edge refinement module (ER) are proposed to further suppress the interference in the local feature maps and refine the edge features of the pavement crack defects. Finally, a multi-scale feature enhancement (MFE) module is presented for multi-scale attention feature representation, by which we can provide high-quality input features for the decoding side. After extensive experimental validation, our proposed model has demonstrated a superior performance over existing mainstream models on multiple pavement crack defect segmentation datasets. The code of the model has been open to: https://github.com/MMYZZU/Crack-Segmentation.