# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 1 Introduction
Specifically, semantic segmentation models classify each pixel in an image into predefined categories, demonstrating promising results across visual inspection tasks. This approach holds significant engineering value for evaluating crack morphological features and quantifying damage severity. By performing pixel-wise recognition, semantic segmentation captures precise crack contours. For instance, [PERSON] et al. (2018) proposed a U\({}^{2}\)-Net-based method for underwater pier defect identification, achieving high-precision contour and dimension recognition with a maximum Intersection over Union (IoU) of 0.73. To mitigate background interference, [PERSON] et al. (2018) developed a two-stage method for underwater concrete piers: defect localization using YOLOv7 and defect segmentation with an enhanced DeepLabV3+ network; this approach attained mean IoU scores of 0.914 for identifying exposed rebar and spalling. Faced with the practical challenge of limited underwater defect data acquisition, researchers have adopted alternative approaches by leveraging transfer learning strategies ([PERSON] et al., 2018; [PERSON] et al., 2018) to break through data bottlenecks, offering innovative solutions for precise segmentation in low-resource scenarios. For instance, [PERSON] et al. (2018) developed a real-time framework for underwater crack segmentation and quantification using a lightweight LinkNet network combined with hybrid transfer learning, enabling efficient identification in complex underwater environments. Beyond architectural improvements, cutting-edge research explores novel pathways integrating physical prior knowledge: [PERSON] et al. (2018) pioneered a \"knowledge-guided recognition\" paradigm by combining fractal characteristics of cracks with deep learning. Through fractal dimension matrix calculations, they extracted fractal features of cracks, which were then utilized as prompt inputs for the Segment Anything Model (SAM) to establish a plug-and-play defect segmentation method. Thisapproach achieved remarkable metrics for underwater cracks: average accuracy--97.6%; IoU--0.89; F1 score--0.95. Recent deep learning models like SCTNet [25] and LarvSeg [26] have advanced crack segmentation but exhibit critical limitations in underwater environments. SCTNet relies on static convolutional kernels that fail to adapt to turbidity-induced texture variations, suffering IoU degradation in murky conditions. LarvSeg's scale-invariant backbone lacks hierarchical feature refinement, limiting small crack detection to smaller recall for sub-millimeter defects. Crucially, no existing method simultaneously addresses turbidity distortion and extreme scale variance--a fundamental gap for underwater infrastructure inspection where <1 mm cracks in low-visibility conditions signify critical structural risks. However, the application of semantic segmentation to underwater crack detection remains underexplored. The challenges posed by underwater environments--such as significant light scattering and absorption, poor contrast, and the need to identify fine-scale defects--necessitate improvements to the existing models. Among them, DeepLabV3+ has become a widely adopted architecture due to its efficiency in segmenting complex images, capturing both local and global features [27]. To bridge this gap, this paper proposes an enhanced DeepLabV3+ framework with three innovations: (1) Physics-grounded light propagation correction: Our depth-dependent model uniquely simulates bidirectional scattering distribution functions (BSDF) to compensate wavelength-specific attenuation. Unlike empirical enhancement methods, it preserves crack topology through turbidity-adaptive histogram reweighting. (2) Hierarchical multi-scale decoder: By integrating learnable scale weights and skip-connected ASPP modules, this paper dynamically adjusts receptive fields from 1 mm to >5 cm cracks, resolving LarvSeg's fixed-scale limitation.