# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 2 Methods  
### DeepLabV3+ Architecture and Training Framework  
DeepLabV3+ is a state-of-the-art CNN architecture designed for semantic segmentation tasks [28]. It utilizes an encoder-decoder structure, where the encoder captures high-level semantic information, and the decoder refines the segmentation output (as shown in Figure 1). One of the key features of DeepLabV3+ is its use of atrous convolution [29], which allows the network to capture features at multiple scales by adjusting the receptive field. This ability to capture both local and global context makes DeepLabV3+ well-suited for tasks such as crack detection in underwater environments, where cracks can vary significantly in size and shape.  
The proposed system builds on DeepLabV3+ [28], featuring an encoder-decoder structure with atrous spatial pyramid pooling (ASPP). As shown in Figure 1, the encoder extracts multi-scale features using dilated convolutions with dilation rates [6,12,18], while the decoder refines segmentation masks through progressive upsampling [29]. This paper uses MobileNetV2 as the backbone, initialized with ImageNet weights but adapted for underwater environments through domain-specific modifications.  
In the original DeepLabV3+ model, the encoder is typically based on pre-trained backbones like Xception or MobileNetV2 [30], which provide strong feature extraction capabilities. However, these models were not specifically designed for underwater imaging conditions, which involve light distortion, color imbalance, and reduced visibility. Therefore, several modifications are proposed to enhance the performance of DeepLabV3+ for underwater crack detection.  
The proposed model provides optimal initialization for our innovations in light modeling (Section 2.2), multi-scale extraction (Section 2.3), and curvature guidance (Section 2.4).  
To train the proposed model (as shown in Figure 2), a dataset consisting of underwater crack images was collected from various environments, including natural underwater structures and man-made infrastructures. Given the limited availability of labeled underwater crack datasets, this paper employs data augmentation techniques such as random rotations, flipping, and varying illumination conditions to artificially expand the training set. This ensures that the model generalizes well to different underwater conditions and crack types.  
Figure 1: DeepLabV3+ framework.  
Figure 2: Proposed framework.  
A two-stage training process was implemented: first, the model was pre-trained using a large dataset of general underwater images to learn basic features of underwater environments, and then the model was fine-tuned using the crack detection dataset to focus on the specific task of crack segmentation.  
In summary, although the components proposed in this study are derived from foundational theories in image processing and geometry, our contribution lies in their novel task-specific integration and mathematical formalization. This includes a physically grounded preprocessing module, a hierarchical multi-scale fusion decoder, and a curvature-weighted optimization loss--all embedded within a unified DeepLabV3+-based architecture for underwater crack detection.