# Underground Mine Road Detection Using Deep Learning Technique
## Materials and Methods  
The detection process of the drivable area of the underground mine road image is shown in Figure 1. It mainly includes two stages of training and application. In the training stage, the mine road image is collected by the camera mounted in front of the mine car, and the dataset is expanded by the data enhancement method, and the driving area of the underground mine road is marked. The underground mine road image dataset is put into the semantic segmentation network for training. When the loss value converges, the model is trained. In the application stage, the driving area of the underground mine road can be detected by inputting the image of the underground mine road into the trained model. BiSeNet is used as the basic network model for semantic segmentation of underground mine slopes. The BiSeNet network designs two branch networks, namely Spatial Path and Context Path (CP). Aiming at the problem of spatial information loss in semantic segmentation, the spatial path solves the problem of spatial information loss by acquiring more low-level features, thus encoding rich spatial information, while the context path provides a larger receptive field. The main structure of the BiSeNet network is shown in Figure 2. There are 3 layers in the spatial path. The size of the convolution kernel of the first layer is 7 \(\times\) 7, and the size of the convolution kernel of the last two layers is the same as 3 \(\times\) 3. Each layer has to undergo convolution with a stride of 2. Using convolution (CONV), batch normalization (BN) and a ReLU nonlinear activation operation, the image size output by this path is 1/8 of the original image, and the context path provides enough experience and semantic information to the network. The attention refinement module (ARM), based on the global average pooling to obtain the global context, guides feature learning by calculating the attention vector, and refines the output features of the context path. Obtaining global contextual information through an attention optimization module can improve image segmentation accuracy. The model designs a feature fusion module (FFM) to fuse spatial features and contextual information to improves the feature extraction capability. The fusion layer uses bilinear interpolation to upsample the feature map size extracted by the context path to the same size as the feature map obtained by the spatial path, and then these features are fused through the feature fusion layer. This module pools connected features into feature vectors and computes weight vectors, which are used to re-weight the features to output features that combine spatial and contextual paths. Figure 1: Underground mine road image segmentation flow chart. The underground mine road environment is complex, and has problems such as uneven roads, obstacles, and insufficient illumination. The mainstream deep learning model, although high in accuracy, cannot run complex deep learning network models due to the limited computing power of the mine car's on-board computer. Therefore, in the face of the complex mine road environment, it is difficult to meet the real-time demand. BiSeNet's semantic segmentation algorithm uses a lightweight backbone specially designed for classification tasks, and cannot give full play to the performance of the BiSeNet algorithm in the field of semantic segmentation. Due to the complex environment of underground mines and poor light conditions, BiSeNet using the classification backbone network cannot accurately identify roads. For the situation of underground mine roads, the network needs to be improved. STDC is a real-time semantic segmentation network proposed by [PERSON], which gradually reduces the dimension of feature maps and uses aggregated information to represent images [30]. On BiSeNet, this study replaced the backbone network with STDC.