# Underground Mine Road Detection Using Deep Learning Technique
###### Abstract  
Semantic segmentation of underground mine roads is very important to efficiently obtain road information from images. The boundary of underground mine roads is not obvious, the environment is complex, and road identification is difficult. In order to effectively realize the accurate identification of underground mine roads, a network identification model using a deep learning technique is proposed. Choosing BiSeNet as the basic framework, adopting a unified attention fusion module, and using channel and spatial attention to enrich the fusion feature representation can effectively obtain feature information and reduce the loss of feature information. In addition, the lightweight network STDC is integrated into the backbone network to reduce computational complexity. Finally, experiments were carried out on underground mine roads. The experimental results show that the mean intersection over union and pixel accuracy of the proposed method reached 89.34% and 98.34%, respectively, and the recognition speed reached 23 f/s when identifying underground mine roads. In this study, the underground mine road recognition model trained by deep learning technology can solve the problem of underground mine road recognition with high accuracy. deep learning; mine road; semantic segmentation; mine road identification; BiSeNet +
Footnote †: journal: Medical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China  
Aiming at the problems of poorly defined road edges and the distant road segmentation effect, [PERSON] et al. improved the network feature extraction capability by integrating the attention mechanism, and adopted a lightweight feature extraction network to greatly reduce the amount of model parameters under the premise of ensuring the recognition accuracy [15]. However, its accuracy and robustness are still poor. The mining area is a relatively special scene for the application of autonomous driving. The poor conditions of the mine are mainly manifested in poor road conditions, more gravel and more dust in the mining area. Many mines are mined along the mountain; the road construction is difficult and the service life is short. Therefore, the road surface of the mining area is mostly paved with gravel, and the road conditions are complex, which is more likely to damage vehicle parts and shorten vehicle life. In addition, there is much dust in the mining area, and the dust particles are large. The sensing equipment is easily covered by dust and this affects the environmental perception ability of the unmanned mine car. In addition to the impact of the complex road environment on the unmanned mine car, there are also some problems in the versatility, reliability and safety of the technology. First, in different mining areas, the mine car will fail due to some unique situations; the electromagnetic characteristics of metal ore will affect the performance of millimeter wave radar, and the temperature of alpine mining areas will affect the stability of electrical and electronic components. Second, the environment of the mining area is complex and harsh. Dust, flying stones, and so on pose a great threat to the perception layer of the unmanned mine car. The probability of the perception equipment being hit in a mining area with many rockfalls greatly affects its safety. In view of the challenges of mine roads, some researchers have also carried out extensive work [16; 17; 18; 19; 20; 21; 22]. [PERSON] et al. improved the pyramid attention module of the original bilateral network and the channel attention fusion module [23; 24] for the detection of underground coal mine tracks, which improved the detection accuracy. [PERSON] and others realized the real-time identification of the central area, left safety area and right safety area of the underground track by improving the BiSeNet network [25].