Bridge Crack Segmentation Algorithm Based on Improved U-Net

This paper introduces an enhanced U-Net network designed to extract fine crack features against complex backgrounds and balance crack pixel categories for bridge crack segmentation. Initially, the model integrates an Efficient Channel Attention module with the original U-Net to dynamically calibrate channel features. This enhances the detection of crack-related features and suppresses irrelevant textures. The architecture is further enhanced by a Dynamic Snake Convolution layer, which employs learnable offset vectors to adaptively deform convolutional kernels. This allows for precise mapping along the contours of curvilinear cracks. Finally, the model utilizes a weighted combination loss function to achieve a balance between pixel-level classification and global shape consistency, effectively addressing gradient conflicts in scenarios with class imbalance. The effectiveness of this algorithm was validated on a custom dataset, demonstrating superior performance over existing methods with a 1.23% increase in mIoU and a 1.89% improvement in F1-Score compared to the original U-Net, thus meeting the demands of practical engineering applications.