# Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network
## 6 Conclusions  
In the domain of hydropower station infrastructure safety, traditional manual inspection and crack detection methods based on conventional digital image processing are plagued by issues such as low efficiency and dependence on manual feature design. Current deep learning models encounter challenges in plunge pool corridor crack detection, including a lack of datasets, complex crack morphologies, and inadequate cross-dimensional semantic information extraction capabilities. To tackle these challenges, this study employs an inspection robot to acquire full-area crack data in plunge pool corridors and establishes a plunge pool corridor crack dataset comprising 4075 images through data augmentation algorithms applied to the original data. Through investigating the effects of integrating morphology-integrated irregular dynamic convolution (MIDConv) and the cross-dimensional interactive attention module (CDIAM) on model feature extraction, a pixel-level crack segmentation model based on an improved U-Net network tailored for this corridor dataset is proposed. The performance of the improved U-Net model is thoroughly validated via quantitative metrics and visual analysis. 1. Quantitative assessments reveal that the improved model achieves an MIoU, F1-score, MPA, and accuracy of 84.44%, 93.05%, 89.75%, and 99.46%, respectively. When compared with mainstream semantic segmentation models like DeepLabV3+ and U2 Net, it exhibits superior fine-grained segmentation capability for overlapping areas between crack regions and backgrounds. The notable enhancement in F1-score signifies that the model has achieved breakthroughs in balancing precision and recall, effectively minimizing the missed detections of slender cracks (e.g., missing bifurcation structures) and misjudgments of complex backgrounds (e.g., stains, uneven lighting areas) of traditional models. 2. Visual comparisons further disclose that the improved model, through the morphology-integrated irregular dynamic convolution (MIDConv), binds the convolution kernel's offsets in the X/Y axis directions with the linear morphological features of cracks, allowing the receptive field to dynamically expand along crack orientations and thus fully capture continuous edges. Simultaneously, the cross-dimensional interactive attention module (CDIAM) significantly enhances the capability to model pixel-level semantic relationships by partitioning channels into sub-feature groups and calibrating cross-dimensional weights via global average pooling, effectively suppressing background interference and retaining crack details. 3. Independently introducing the MIDConv or CDIAM module elevates the MIoU to 82.78% and 82.36%, respectively, whereas their combination further boosts the metric to 84.44%, demonstrating significant complementarity between dynamic feature extraction and cross-dimensional semantic enhancement. In generalization capability tests, the improved model validates its accuracy-efficiency balance advantage--achieved via structured feature group partitioning and convolution kernel offset constraints--on public datasets. For future work, plans involve expanding multimodal datasets, optimizing model architecture, exploring cross-scenario transfer learning, and developing an integrated detection-repair system to further improve model performance and practical application value. **Author Contributions:** Conceptualization, X.Z. and Y.S. ; writing--original draft preparation, X.Z. and Y.S. ; writing--review and editing, Y.S. ; supervision, Y.L. ; data curation, C.H., Z.L. and F.X. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the Three Gorges Jinsha River Chuanyun Hydropower Development Co., Ltd. Yongshan Xiloudu Power Plant, No. Z412302022; the National Natural Science Foundation of China, No. U21A20157; and the Sichuan Science and Technology Program No. 2025 ZHCG0016. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.