# Underground Mine Road Detection Using Deep Learning Technique
## 3 Experiment and Results
### Model Training  
Figure 11 shows the change curve of the loss value during the training of the network, and the mIoU change is shown in Figure 12. In the process of network training, as the number of iterations continues to increase, the loss value continues to decrease. When the number of iterations reaches 20,000, the loss value reaches a minimum of about  
Figure 10: Data augmentation. SegNet is a classic semantic segmentation network. In this experiment, mIoU reached 86.55%, and the accuracy rate is the second highest, reaching 97.81%. Its achievement lies in its encoder-decoder structure. The way the decoder performs upsampling directly uses the pooling index when performing the maximum pooling in the corresponding encoder stage to perform non-linear upsampling. However, the frames per second of SegNet is only 2 f/s, which is the slowest among the four networks. The mIoU of ENet reached 85.34%, and the pixel accuracy reached 97.53%, which is similar to that of SegNet. Because the ENet network is relatively light, its operating speed reached 13 f/s, which is much faster than that of SegNet, but it is still not sufficient for real-time purposes. The mIoU of BiSeNet is the lowest, only 74.83%, the Dice is also the lowest, only 83.22%, and the frame rate is only 7 f/s. Compared with SegNet and ENet, BiSeNet has almost no advantages. It can be seen from the data in the table that the improved BiSeNet in this paper has obvious advantages in the mean intersection over union and pixel accuracy. The improved BiSeNet, due to the replacement of the backbone and the addition of a unified attention fusion module, mIoU and fps are 89.34% and 13 f/s, respectively, which have been greatly improved. Compared with the original BiSeNet, the mean intersection over union and pixel accuracy are improved by 19% and 3%. Compared with the previous network, the segmentation effect is the best, and can meet the real-time requirements. The frame rate of the network in this paper can reach 23 f/s, which basically meets the requirements of real-time recognition. The experimental results show that the improved network segmentation rate has been significantly improved, and compared with other networks, the accuracy is also higher, and it has a better segmentation effect. In order to illustrate the effectiveness of replacing the backbone network and adding the UAFM module, we compared the performance of the network before and after the replacement on underground mine roads, as well as the performance after adding a unified attention fusion module, and the mIoU improvement after replacing the backbone network with STDC. After adding the UAFM on this basis, the mIoU increased by 2.08%. Table 3 shows the specific experimental results. Figure 13 includes the following images: (a) shows the input color image; (b) shows the label; (c) shows the target for road recognition in underground mines; (d)-(g) show the results of different network models. The continuous identification of underground tunnels can explain the robustness of the model. Therefore, we conducted continuous identification experiments on underground mine tunnels. Figure 14 is a comparison chart of the mine road recognition results at different time points. It can be seen that the improved network has a better effect on identifying underground mine slope roads. \begin{table}
\begin{tabular}{c c} \hline
**Method** & **mIOU/\%** \\ \hline BiSeNet & 74.83 \\ BiSeNet + STDC & 87.26 \\ BiSeNet + STDC + UAFM & 89.34 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of the effect of replacing the backbone network and adding UAFM. The external environment also affects the recognition of underground mine roads, and the underground mine road environment is special, and there is wind and sand blowing from time to time, which has a certain impact on the camera. Moreover, there are more straight roads and fewer curves in underground mine roads. Therefore, in order to prove the effectiveness of the proposed method, we need to consider the road environment.