# Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network
## 1 Introduction  
The corridors of hydropower stations are critical components of hydraulic hubs, directly affecting the safety and economic benefits of the hydropower station. The appearance of cracks threatens structural integrity and can lead to water leakage and equipment damage. Therefore, regular crack detection is crucial as it enables the timely identification and resolution of potential risks, ensuring the health and stability of the corridors and thereby safeguarding the safe operation and efficient maintenance of the hydropower station. Conventional manual inspection not only involves substantial consumption of human and material resources but also suffers from extremely low detection efficiency, being unable to meet the requirement of rapid detection [1]. Alongside the rapid advancement of computer science, the accurate detection of concrete cracks via computer vision has emerged as a primary research goal. [PERSON] et al. [2] employed the Sobel operator to detect crack edges in images, identifying genuine cracks through region filling, labeling, and adjacent region connection operations. [PERSON] et al. [3] utilized a wavelet-based algorithm to eliminate noise from images and integrated the C-V model with a Canny edge detector for crack segmentation. Through comparison with the original Canny edge detector, the proposed method outperformsthe original Canny edge detector in performance. Likewise, [PERSON] et al. [4] employed the Canny edge detector to extract cracks from images and optimized crack shapes with a region-based active contour model. Subsequently, support vector machines (SVM) and a greedy search strategy were applied to eliminate noise induced by crack surface contamination and uneven lighting, ultimately yielding pixel-level crack detection results with distinct shapes. Despite extensive research on crack detection using conventional digital image processing techniques, their limitations are evident. Conventional image processing techniques depend on manual feature extraction and segmentation, restricting their applicability to specific scenarios. In recent years, convolutional neural networks (CNN) [5] have been widely applied in various tasks, with segmentation techniques based on fully convolutional networks demonstrating exceptional performance in infrastructure inspection, such as concrete crack detection, providing strong technical support for crack recognition and monitoring. The U-Net model [6], originally developed for the medical field, along with its improved variants, has been widely applied in concrete infrastructure inspection, including residential building crack detection [7; 8; 9; 10; 11], bridge surface crack detection [12; 13; 14], and highway surface crack detection [15; 16; 17; 18; 19; 20; 21]. For instance, [PERSON] et al. [22] integrated dilated convolution and spatial pyramid pooling modules into the deep fully convolutional neural network to enable efficient multi-scale feature extraction, aggregation, and resolution reconstruction, demonstrating high precision and generalization capability in crack detection. Aiming at complex scenarios in tunnel images, including water stains, scratches, and structural joints, [PERSON] et al. [23] developed an objective and fast recognition algorithm for tunnel cracks via computer vision-based semantic segmentation. [PERSON] et al. [24] developed the SDDNet network, comprising standard convolution, a DenSep module, an improved atrous spatial pyramid pooling (ASPP) module, and a decoder module. Experimental results indicate that SDDNet is highly effective for segmented crack segmentation. [PERSON] et al. [25] opted to learn and aggregate multi-scale and multi-level features from low to high convolutional layers during training, followed by guided filtering and conditional random fields (CRFs) for model optimization, resulting in the DeepCrack deep neural network with an F1-score of 86.5%. The CrackU-Net proposed by [PERSON] et al. [26] enhances the depth of the U-Net model and surpasses the original U-Net in pavement crack detection.