Coal Wall and Roof Segmentation in the Coal Mine Working Face Based on Dynamic Graph Convolution Neural Networks

Zhizhong Xing; Shuanfeng Zhao; Wei Guo; Xiaojun Guo; Shenquan Wang; Junjie Ma; Haitao He

doi:10.1021/acsomega.1c04393

Coal Wall and Roof Segmentation in the Coal Mine Working Face Based on Dynamic Graph Convolution Neural Networks

ACS Omega. 2021 Nov 15;6(47):31699-31715. doi: 10.1021/acsomega.1c04393. eCollection 2021 Nov 30.

Authors

Zhizhong Xing¹, Shuanfeng Zhao¹, Wei Guo¹, Xiaojun Guo², Shenquan Wang¹, Junjie Ma¹, Haitao He^{1

3}

Affiliations

¹ College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
² School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641, China.
³ Shendong Coal Group Co., Ltd. of National Energy Group, Yulin 719315, China.

Abstract

The intersection line information of the point cloud between the coal wall and the roof can not only accurately reflect the direction information of the scraper conveyor but also provide a preliminary basis for realizing the intelligent coal mine. However, the indirect method of using deep learning to segment the point cloud of coal mine working face cannot make full use of the rich information provided by the point cloud data. The direct method of using deep learning to segment the point cloud ignores the local feature relationship between points. Therefore, we propose to use dynamic graph convolution neural networks (DGCNNs) to segment the point cloud of the coal wall and roof so as to obtain the intersection line between them. First, in view of the characteristics of heavy dust and strong electromagnetic interference in the environment of the coal mine working face, we have installed an underground inspection robot so that we use light detection and ranging to obtain the point cloud of the coal mine working face. At the same time, we put forward a fast labeling method of the point cloud of the coal mine working face and an efficient training method of the depth neural network. Second, on the basis of edge convolution, being the greatest innovation of DGCNNs, we analyze the influence of the number of layers, K value, and output feature dimension of edge convolution on the effect of DGCNNs segmenting the point cloud of the coal mine working face and obtaining the intersection line of the coal wall and roof. Finally, we compare DGCNNs with PointNet and PointNet++. The results show that the DGCNN exhibits the best performance. What is more, the results provide a research foundation for the application of DGCNNs in the field of energy. Last but not least, the research results provide a direct and key basis for the adjustment of the scraper conveyor, which is of great significance for an intelligent coal mine working face and accurate construction of a geological information model.