# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 4 Results  
### Quantitative Results  
This paper provides a detailed comparison of our proposed model's performance with various other state-of-the-art segmentation models. The training processes of different models are shown in Figure 1. The results are shown in several tables, each highlighting different aspects of the evaluation. In addition to the baseline DeepLabV3+ model, the proposed method was compared with SCTNet, LarvSeg, and the original DeepLabV3+. Table 1 provides the overall performance comparison across multiple models, including key segmentation metrics: Intersection over Union (IoU), F1 score, Pixel Accuracy, and Crack Detection Accuracy.  
The Enhanced DeepLabV3+ (Proposed) model consistently outperforms the baseline and other models across all the metrics. The \(IoU\) is increased by 10.6% compared to the original DeepLabV3+, and the Crack Detection Accuracy improves by 11.1%, indicating  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **IoU (\%)** & \(p\)**-Value** & **F1 Score (\%)** & \(p\)**-Value** & \
\begin{tabular}{c} **Pixel** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Crack** \\ **Detection** \\ **Accuracy (\%)** \\ \end{tabular} \\ \hline Original & 71.9 & \textless{}0.001 & 76.8 & \textless{}0.001 & 83.6 & 68.7 \\ DeepLabV3+ with & & & & & & \\ Light Model & 76.3 & 0.003 & 80.8 & 0.002 & 87.6 & 73.1 \\ Enhanced & & & & & & \\ DeepLabV3+ & 82.5 & 0.008 & 85.6 & 0.009 & 90.3 & 79.8 \\ (Proposed) & & & & & & \\ \hline SCTNet (2024) & 70.4 & \textless{}0.001 & 76.1 & \textless{}0.001 & 81.6 & 66.1 \\ \hline LarvSeg (2025) & 69.1 & \textless{}0.001 & 74.3 & \textless{}0.001 & 80.7 & 63.9 \\ \hline Mask2 Former (2021) & 75.1 & 0.002 & 80.2 & 0.002 & 85.6 & 72.4 \\ \hline Swin-UNet (2021) & 76.8 & 0.003 & 81.5 & 0.003 & 86.9 & 74.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison results of multiple models.  
that the model can more accurately identify and segment cracks, even in complex underwater environments. Table 2 shows the breakdown of performance by crack size to better understand the model's detection capabilities across different crack scales. Figure 7 shows examples of detecting cracks of different sizes. Large cracks maintain excellent detection performance, while thinner cracks may lose a small amount of detail, but the overall contour remains good.  
The model demonstrates particular strength in detecting small cracks, which is one of the key challenges in underwater crack detection. The IoU for small cracks improves by 12.5%, suggesting that the multi-scale feature extraction and curvature flow-based guidance are particularly beneficial in capturing fine-grained features of cracks. Table 3 includes additional information on the model's robustness under varying underwater conditions, such as turbidity and low-light environments. The performance degradation (i.e., drop in IoU and F1 score) was measured as water turbidity increases or light intensity decreases.  
Even in the presence of turbidity or low light, the enhanced model significantly outperforms the original DeepLabV3+ model, with a degradation of only \(-\)3.6%-8.8% compared to \(-\)4.5-10.4% for the original model. This demonstrates the robustness of the proposed method under challenging underwater conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Crack Size** & **Original DeepLabV3+ IoU** & **DeepLabV3+ with Light** & **Enhanced DeepLabV3+ IoU** \\  & **(\%)** & **Model IoU (\%)** & **(\%)** \\ \hline Small Cracks & 65.6 & 70.8 & 78.1 \\ \hline Medium Cracks & 74.5 & 78.3 & 85.2 \\ \hline Large Cracks & 80.8 & 83.3 & 89.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of different crack scales.  
Figure 7: Comparative detection performance across crack scales: (**a**) small cracks showing enhanced boundary precision; (**b**) medium cracks demonstrating continuity preservation; (**c**) large cracks with accurate topology reconstruction.