Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network

Zhizhong Xing; Shuanfeng Zhao; Wei Guo; Xiaojun Guo; Yuan Wang; Yunrui Bai; Shibo Zhu; Haitao He

doi:10.1021/acsomega.1c05473

Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network

ACS Omega. 2022 Feb 1;7(6):4892-4907. doi: 10.1021/acsomega.1c05473. eCollection 2022 Feb 15.

Authors

Zhizhong Xing¹, Shuanfeng Zhao¹, Wei Guo¹, Xiaojun Guo², Yuan Wang¹, Yunrui Bai¹, Shibo Zhu¹, 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

In the process of coal mining, a certain amount of gas will be produced. Environmental perception is very important to realize intelligent and unmanned coal mine production and operation and to reduce the accident rate of gas explosion and other disasters. The identification of geometric features of the coal mine working face is the main part of the environmental perception of the working face. In this study, we identify geometric features in a large-scale coal mine working face point cloud (we take the ball as an example) so as to provide a method for the environmental perception of the coal mine working face. On the basis of previous research, we upgrade the dynamic graph convolution neural network (DGCNN) for directly processing point clouds from two aspects: extracting local features and global features of point clouds. First, a multiscale dynamic graph convolution neural network (MS-DGCNN) is proposed, and the combination of max-pooling and average-pooling is used as the symmetry function. Second, we use MS-DGCNN to learn the features of a variety of geometric point clouds in the point cloud data set we make and then look for the ball in the large-scale point cloud of the coal mining working face. Finally, we compare the performance of MS-DGCNN with that of other deep neural networks directly processing point clouds. This study enables MS-DGCNN to obtain more powerful feature expression ability and enhance the generalization of the model. In addition, this study provides a solid foundation for the geometric feature identification of MS-DGCNN in the environmental perception of the coal mine working face and creates a precedent for the application of MS-DGCNN in the field of energy. At the same time, this study makes a beneficial exploration for the development of a transparent coal mine working face.