# Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network
## 1 Introduction
To tackle the issue of crack discontinuities in segmentation outcomes, [PERSON] et al. [27] introduced the HC-Unet++ network segmentation model for road crack detection. [PERSON] et al. [28] incorporated a multi-scale attention module into the decoder of DeepLabv3+, which effectively reduces the instances of missed crack detections. * Semantic segmentation approaches are capable of achieving detailed identification of specified objects, showcasing notable advantages in concrete crack detection. Yet most current mainstream crack detection networks are tailored for scenarios like building walls, road surfaces, or bridges. Plunge pool corridors, long-term exposed to high-speed water flow erosion, thermal stress, and wet-dry cycles, exhibit concrete cracks with morphological features, spatial distributions, and data distributions that differ substantially from target objects in conventional public datasets. This makes it challenging for generic models to accurately differentiate targets from backgrounds. Additionally, the nonlinear morphology and multi-scale characteristics of cracks in plunge pool corridors render traditional fixed-size convolution kernels ineffective in capturing edge details. While techniques like deformable convolutions attempt to dynamically adjust the receptive field, existing approaches lack the exploitation of crack morphological priors, making them susceptible to feature deviation in complex backgrounds and resulting in segmentation issues such as discontinuities and missed detections. To address the aforementioned research gaps, this study develops a specialized dataset for crack segmentation in plunge pool corridors via on-site data collection and presents an improved U-Net model incorporating morphology-aware dynamic convolution and cross-dimensional interactive attention. The contributions of this work are as follows:  
1. Development of a scenario-specific dataset: Using an inspection robot outfitted with a multi-angle vision system, we acquired crack images of plunge pool corridors with complex backgrounds and constructed a high-quality dataset comprising 4075 images via data augmentation techniques, addressing the lack of data for plunge pool corridor scenarios. 2. Proposing morphology-integrated irregular dynamic convolution (MIDConv): By integrating the linear morphological features of cracks and constraining the offset accumulation of deformable convolutions, the receptive field of the convolution kernel is dynamically expanded along the crack orientation, effectively enhancing the ability to extract features of irregular edges. 3. Development of cross-dimensional interactive attention module (CDIAM): By partitioning channels into groups and introducing a global information calibration mechanism, semantic interaction between channel and spatial dimensions is established, enhancing the model's capacity to model pixel-level relationships of cracks in complex backgrounds and markedly reducing misdetections and omissions. 4. Substantial enhancement in detection performance: On the self-constructed dataset, the improved model achieves an MIoU, F1-Score, MPA, and accuracy of 84.44%, 93.05%, 89.75%, and 99.46%, reflecting improvements of 5.34%, 4.90%, 3.39%, and 0.36% compared to the original U-Net, effectively resolving the problem of inadequate detection accuracy of traditional methods in complex scenarios. Additionally, to validate the generalization capability of the proposed approach, experiments were performed on two public datasets, both yielding promising results.