## 4 Experimental Verification
### Pressure Relief Hole Image Generation Test
#### 4.2.2 Results and Analysis  
In order to explore the performance of improved SinGAN in generating image diversity, 221 image samples of pressure relief holes were collected for testing and analysis. All relief hole images were trained in the traditional SinGAN model, our improved SinGAN model, and SinGAN in [25], respectively. At the same time, to show the comparison effect, three real images were randomly selected from the relief hole data set. After three iterations of network model training, four images were selected from the generated images for display, as shown in Figure 10. Subjectively, compared with the traditional SinGAN, the image quality generated by the improved SinGAN model had more detail and more diversity.  
In order to objectively evaluate the effect of image generation, two indicators, structural similarity (SSIM) and Single Image Frechette starting distance (SIFID), were selected for evaluation. SSIM was mainly used to measure the similarity between two images, and evaluate the distortion between images from the perspectives of brightness, contrast and structure, so as to obtain a more objective image quality assessment. The value range of SSIM was 0-1, and the index value from small to large represented the fact that the similarity between the reconstructed image and the real image was growing higher and higher. SIFID was specially used to evaluate the quality of the generated image, and the value was generally 0-1. The closer in quality the generated image and the original image are, the lower the index score is. Due to limited space, 20 groups of comparative data are randomly listed, as shown in Table 1.  
Figure 10: Images generation results based on traditional SinGAN, improved SinGAN in [25], and our improved SinGAN.  
Generally, the underground images of coal mines are divided into two categories: structure and texture. If there are prominent blocky features in the image, it means that its structure is strong. If the surface of the image is relatively flat, it indicates that it has strong texture. However, most relief hole images have obvious structure. Therefore, when the SSIM index is lower, it indicates that the data structure of the generated image has changed significantly. When the SSIM index is higher, it indicates that the features of the generated image are closer to the original image, and that the diversity of the generated image is rather poor. As can be seen from Table 1, the SIFID values of most images generated by improved SinGAN are lower than traditional SinGAN and improved SinGAN in [25], which proves that the improved model can obtain better image quality. Moreover, the SSIM values of the images generated by improved SinGAN are also lower than those of traditional SinGAN and SinGAN in [25], indicating that the improved model's generated images have better diversity. In summary, the improved SinGAN model can not only enhance the diversity of generated images, but also ensure the quality.  
In order to further explore the performance of the improved SinGAN model, the training duration of the generated image was analyzed. Three SinGAN models, including traditional SinGAN, our improved SinGAN, and SinGAN in [25], were used to generate image samples separately 50 times, and the required training time is shown in Figure 11. It can be seen that the average training time of the improved SinGAN model is 1.15 h, which is 77.93% lower and 86.91% lower than SinGAN in [25] (5.21 h) and the traditional model (8.79 h). The results indicate that improved SinGAN can generate higher-quality images in a shorter time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Traditional SinGAN**} & \multicolumn{2}{c}{**Improved SinGAN in [25]**} & \multicolumn{2}{c}{**Our improved SinGAN**} \\ \cline{2-7}  & **SSIM** & **SIFID** & **SSIM** & **SIFID** & **SSIM** & **SIFID** \\ \hline
1 & 0.782 & 87.645 & 0.761 & 76.457 & 0.752 & 74.549 \\ \hline
2 & 0.707 & 109.087 & 0.712 & 90.112 & 0.716 & 81.421 \\ \hline
3 & 0