# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.2 Comparative Experiment  
In this section, further comparative experiments were conducted on the Trans10k dataset and the custom dataset to validate and demonstrate the segmentation effectiveness of the proposed method compared to existing algorithms. Similar to many methods utilizing this dataset, the experiments in this section involved randomly splitting the dataset into training, validation, and test sets in proportions of 80%, 10%, and 10%, respectively. Subsequently, by applying five different data augmentation techniques, the Trans10k dataset and the custom dataset were processed, resulting in datasets comprising 52,140 samples and 15,000 samples, respectively. The experimental results of different methods on these datasets are presented in Tables 2 and 3. Tables 2 and 3 show the test results of different semantic segmentation algorithms on the Trans10k dataset and the self-made dataset, respectively. We use three index parameters, namely mIOU, Recall, and Precision, to evaluate the detection results. The WETS-Net proposed in this paper shows excellent performance in the tests of the two datasets, especially in the semantic segmentation task of processing weak edge targets such as glass. On the Trans10k dataset, WETS-Net achieved the highest values in the two key indicators of mIOU and Precision, reaching 78.42% and 95.67%, respectively, which shows that the model can accurately identify and locate the target area while maintaining a low false alarm rate. Although it is slightly lower than LeViT-UNet in the Recall index, overall, WETS-Net's overall performance on this dataset is more comprehensive and balanced. It is worth mentioning that on the self-made dataset, WETS-Net is significantly better than other methods in all evaluation indicators, with mIOU, Recall, and Precision reaching 84.23%, 90.23%, and 96.13%, respectively, which fully proves the robustness and generalization ability of the model in practical application scenarios. In contrast, the performance of LeViT-UNet on the self-made dataset has declined, especially in terms of detection accuracy, which is 6.01% lower than that of WETS-Net. This may be due to the presence of more complex backgrounds and interference factors in the dataset, which puts higher requirements on the model's feature extraction and fusion capabilities. The segmentation effect of WETS-Net is shown on our self-made dataset (as shown in Figure 9). Obviously, when processing images with obvious edge features of a single target, the segmentation effect of each network is quite good. However, when facing glass images with a dark background, various segmentation algorithms show certain deviations. Especially in this case, the model proposed in this paper and the LeViT-UNet model performs better than other algorithms, further verifying the fact that the introduction of the attention mechanism has a positive effect on the segmentation network. In complex scenes \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **mIOU (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline U-Net & 78.23 & 87.23 & 88.44 \\ ResUNet++ & 81.23 & 88.23 & 90.23 \\ DoubleU-Net & 82.32 & 82.23 & 92.23 \\ LeViT-UNet & 81.32 & 90.88 & 91.12 \\ WETS-Net & 83.83 & 90.43 & 95.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experiments on the Trans10k dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **mIOU (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline U-Net & 71.23 & 85.22 & 87.13 \\ ResUNet++ & 79.23 & 87.61 & 89.97 \\ DoubleU-Net & 80.12 & 81.32 & 89.31 \\ LeViT-UNet & 80.76 & 89.92 & 90.12 \\ WETS-Net & 84.23 & 90.23 & 96.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experiments on self-made datasets. with other foreign objects or diverse objects, similar situations also exist. The effects of various comparison networks do not seem to be ideal, especially when dealing with objects such as transparent glass, which are more susceptible to interference from the background environment.