Crack defects pose a significant threat to the safe operation of the foundational structures of hydropower stations. Therefore, crack detection in a plunge pool corridor is crucial for the safe operation and maintenance of hydropower hubs. Addressing challenges such as missing crack datasets, complex and diverse crack shapes, and difficulties in feature extraction, this paper proposes an improved U-Net model. The model integrates the CDIAM attention module within the U-Net architecture and is optimized through the incorporation of the MIDConv convolutional block. The enhanced U-Net model demonstrates outstanding performance on a self-constructed plunge pool corridor crack dataset, achieving an MIoU, F1-score, MPA, and accuracy of 84.44%, 93.05%, 89.75%, and 99.46%, respectively. Compared to the original U-Net model, these metrics show improvements of 5.34%, 4.90%, 3.39%, and 0.36%, respectively. The improved U-Net model exhibits higher detection accuracy in corridor crack segmentation tasks, effectively extracting feature information from irregular shapes, and provides a superior solution for crack defect detection in a plunge pool corridor, thereby enhancing the safety and efficient maintenance of hydropower stations.