CCT Net: A Dam Surface Crack Segmentation Model Based on CNN and Transformer

Concrete dams are essential components in hydraulic engineering, and the detection of surface cracks remains a major concern in structural health monitoring. This study presents CCT Net, a segmentation model designed to enable the automated detection and segmentation of dam surface cracks. By combining the strengths of convolutional neural networks (CNNs) and Transformers, the model improves the ability to capture fine crack features and global structural patterns, addressing the limitations of single-model approaches. The proposed Feature Complementary Fusion Module enables the effective integration of local and global features, contributing to enhanced segmentation accuracy. The experimental results show that CCT Net achieves high performance on the dam crack segmentation dataset, with the Precision, Recall, F1 score, and mean Intersection over Union (mIoU) reaching 94.5, 93.5, 94.0, and 88.7%, respectively. Compared with traditional CNN-based, Transformer-based, and other existing models, CCT Net demonstrates improved crack segmentation capability.