## 3 Case Study
### Casting Mold Liquid Surface Segmentation Experiment and Result Analysis
#### 3.2.4 Comparative Experiment  
In order to evaluate and examine the effectiveness of the improved DeepLabV3+ model in segmenting the liquid surface of casting models, this study conducted a comparative analysis involving the DeepLabV3+ model, UNet model, and PSPNet model. The comparative results are shown in Table 4.  
The table clearly shows that the upgraded DeepLabV3+ model, as described in this research, outperforms other models in the job of segmenting liquid surfaces. It achieves an _MIoU_ of 91.88%, an _MPA_ of 96.53%, and a _Liquid-IoU_ of 86.97%. The speed indicator surpasses that of other versions, reaching 55.05 FPS. The original DeepLabV3+ method exhibits the second-highest inference speed (37.84 FPS) among all models mentioned in this study. However, its accuracy is the lowest, suggesting that its processing efficiency comes at the expense of accuracy. The UNet network model demonstrates a commendable segmentation accuracy, ranking second with an _MIoU_ of 90.37%. However, it falls behind in terms of speed (24.64 FPS), highlighting the necessity for enhanced processing performance. The PSPNet model has a relatively fast inference speed (34.66 FPS), but its segmentation accuracy is the lowest, measuring only 76.03% at a _Liquid-IoU_ and 84.96% at an _MIoU_. In summary, the improved DeepLabV3+ method presented in this research successfully fulfills the need for accurate and real-time liquid surface segmentation, demonstrating strong overall performance.  
In order to evaluate and compare the effectiveness of the upgraded DeepLabV3+ network against other networks, this study specifically chose casting mold liquid surface photos from the validation set. These images were picked to have different liquid levels, lighting situations, casting units, and aluminum ingot parameters. The segmentation results of different models are shown in Figure 12, where each row depicts the original, annotated, and output images of commonly used segmentation models. The PSPNet model demonstrates a subpar segmentation effect characterized by imprecise delineation of the liquid surface boundary area. It fails to accurately capture the form of the liquid surface and occasionally misclassifies the background as part of the liquid surface. Conversely, the UNet model demonstrates enhanced segmentation outcomes for night-time liquid surface images. However, it encounters difficulties with daytime images, exhibiting fragmented and insufficiently segmented liquid surface regions. The original DeepLabV3+ model also  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Models** & _MIoU_/\% & _Liquid-IoU_/\% & _MPA_/\% \\ \hline DeeplabV3+ (ResNet50) & 90.4 & 84.7 & 95.85 \\ DeeplabV3+ (ResNet-50+CBAM) & 91.0 & 85.55 & 96 \\ DeeplabV3+ (ResNet-50+CBAM+transfer learning) & 91.88 & 86.97 & 96.53 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model ablation experimental results.  
demonstrates instances of both under-segmentation and over-segmentation, along with a limited comprehension of the boundary of the liquid surface. The method presented in this study effectively reduces mis-segmentation, accurately aligns with the ground truth, and exhibits precise judgment for casting mold liquid surfaces in daytime conditions.