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.