# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 1 Introduction  
Underwater infrastructure, including bridges, offshore platforms, and pipelines, plays a crucial role in maintaining global economic activities [1]. However, these structures are susceptible to corrosion, cracks, and other forms of damage caused by constant exposure to harsh underwater environments [2, 3]. Timely identification of such defects is essential to ensure structural integrity and prevent catastrophic failures. Traditional underwater inspection methods, such as human divers and remotely operated vehicles (ROVs), are labor-intensive, time-consuming, and often limited by visibility issues and environmental factors such as murkiness and lighting conditions [4, 5]. Non-optical methods like Lamb wave testing [6, 7] have shown promise in crack detection through guided wave propagation. However, these techniques require direct sensor contact and complex instrumentation impractical for submerged structures, making vision-based approaches essential despite their susceptibility to environmental distortions. With the rise in advanced image processing techniques [8, 9], there has been growing interest in applying computer vision and deep learning models to automate underwater defect detection [10]. In the field of underwater defect detection, traditional methods primarily rely on local statistical approaches and edge detection algorithms to extract defect features. For instance, in local statistical methods, [PERSON] et al. [11] employed fuzzy algorithms to address uneven illumination in underwater visible-light images and proposed a crack identification method combining local features of image patches with global features ofconnected domains. [PERSON] et al. (2018) first enhanced color-distorted images through affine transformation, then achieved precise crack segmentation using multi-directional gray-level fluctuation analysis. Regarding edge detection, [PERSON] et al. (2018) integrated Canny and Sobel operators to determine defect edges, developing an automated method for identifying boundaries of damaged areas. Although these traditional approaches have advanced underwater defect detection to some extent, they still exhibit limitations in adaptability, generalization capability, and automation level when confronting complex and variable underwater environments. The emergence of deep learning technology is now bringing breakthrough advancements to this field. Deep learning models can learn relevant knowledge from large-scale datasets and possess strong generalization capabilities ([PERSON] et al., 2018), maintaining excellent recognition performance even under varying illumination, water quality conditions, or defect types. Simultaneously, deep learning technology automates the underwater defect identification process, significantly enhancing efficiency while reducing human intervention. Research on deep learning-based underwater defect recognition primarily falls into three categories: classification, object detection, and semantic segmentation. For classification tasks, which determine defect presence through the global analysis of images, [PERSON] et al. (2018) improved VanillaNet to enhance classification accuracy for underwater dam crack images. By employing Seesaw Loss to address long-tail distribution issues, they achieved a 2.66% accuracy improvement over the original VanillaNet, outperforming ConvNeXtV2 and RepVGG. In object detection, which precisely localizes defects using bounding boxes, [PERSON] et al. (2018) modified the YOLOv4 framework by replacing CSPDarknet with MobileNetV3 as the feature extraction backbone. They adjusted MobileNetV3's feature layer scales and fed preliminary feature layers into an enhanced feature extraction network for multi-level integration. Compared to the qualitative detection in classification tasks and bounding-box localization in object detection, semantic segmentation provides higher-precision defect characterization through pixel-level recognition ([PERSON] et al., 2018; [PERSON] et al., 2018).