## 5 Conclusions  
1. Aiming at the problem of insufficient diversity and poor performance of the traditional SinGAN model in sample generation, image size adjustment, multi-stage training, and dynamic changes in the number of iterations that are integrated, a sample generation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Traditional Faster R-CNN** & **Improved Faster R-CNN** \\ \hline Number of identified samples & 1124 & 1199 \\ Number of missed samples & 96 & 21 \\ Number of false samples & 155 & 132 \\ Accuracy rate/\% & 87.88 & 90.08 \\ Recall rate/\% & 92.13 & 98.28 \\ Average training time/s & 389 & 250 \\ Average recognition time/s & 0.25 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relief hole target recognition results based on the improved SqueezeNet + BN model with different measures.  
Figure 17: Recognition result graph of underground pressure relief holes based on improved Faster R-CNN model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **YOLO v3** & **YOLO v4** & **YOLO v5** \\ \hline Number of identified samples & 1129 & 1138 & 1161 \\ Number of missed samples & 91 & 82 & 59 \\ Number of false samples & 195 & 182 & 166 \\ Accuracy rate/\% & 85.27 & 86.21 & 87.49 \\ Recall rate/\% & 92.54 & 93.28 & 95.16 \\ Average training time/s & 174 & 184 & 164 \\ Average recognition time/s & 0.021 & 0.027 & 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Relief hole target recognition results based on YOLO v3, YOLO v4, and YOLO v5.  
model for pressure relief hole images based on improved SinGAN was designed. The test results indicate that the designed network model can improve the diversity of generated images on the premise of ensuring the quality of image samples, and can also greatly improve the training speed.
2. Based on the SqueezeNet structure, multi-scale image input and multi-layer feature fusion were introduced, and a target recognition method of relief holes based on improved Faster R-CNN was designed. The ground experiment results show that the accuracy and recall rate of the improved Faster R-CNN model can reach to 90.09% and 98.32%, respectively, and the average detection time is only 0.19s, which verifies the feasibility and superiority of the designed network model in relief hole recognition.
3. The field images were collected from the pressure relief area in an underground coal mine, and the target recognition experiment of the pressure relief hole was carried out. The practical application effect of the improved Faster R-CNN recognition model was tested through transfer learning, and the accuracy and recall rate were able to reach to 90.08% and 98.28%. These values are higher than those of the YOLO v3, YOLO v4, and YOLO v5 models, thus verifying the practicability and feasibility of the proposed method.
4. Due to the complex underground environment and bad working conditions of the coal mine, the number of obtained on-site relief hole images was too small, and a more comprehensive and numerous relief hole image dataset will be built later to improve the accuracy of relief hole recognition. In addition, the relative position relationship between the pressure relief hole and the drilling robot will be further studied in order to improve the positioning accuracy of the drilling system.  
Not applicable.  
**Data Availability Statement:** Data are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.