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

Zhao, Shuanfeng; Guo, Wei; Guo, Xiaojun; Wang, Shenquan; Ma, Junjie; He, Hong

doi:10.1021/acsomega.1c04393

Cited by 7 publications

(3 citation statements)

References 53 publications

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“…Leaky ReLU changes the method of complete inhibition of nonpositive part in ReLU and gives it a smaller slope value. Adaptive motion estimation (Adam) is used in the training process of deep neural network. − …”

Section: Methodsmentioning

confidence: 99%

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

Zhao

Guo

et al. 2022

ACS Omega

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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.

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Section: Methodsmentioning

confidence: 99%

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

Zhao

Guo

et al. 2022

ACS Omega

Self Cite

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“…The tunnel point cloud lacks datasets with semantic labels, and point cloud segmentation models trained on indoor 3D datasets, such as S3D [47] and Scannet [48], which have achieved a good general segmentation results. Xing demonstrated the viability of DGCNN in coal mine tunnel point cloud scenes and established a dataset tailored for coal mine tunnel point cloud segmentation [49]. This research culled data from the intersection between coal wall and the roof of the long-wall working face, offering inspiration for generating point cloud datasets and extracting other pertinent features.…”

Section: Point Cloud Segmentation In Tunnelmentioning

confidence: 99%

A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model

Kang,

Chen,

et al. 2023

Remote Sensing

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In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and insufficient annotated data, contribute to the subpar performance of conventional point cloud segmentation algorithms. To address this issue, a 3D point cloud segmentation framework specifically for underground tunnels is proposed based on the Segment Anything Model (SAM). This framework effectively leverages the generalization capability of the visual foundation model to automatically adapt to various scenes and perform efficient segmentation of tunnel point clouds. Specifically, the tunnel is first sliced along its direction on the tunnel line. Then, each sliced point cloud is projected onto a two-dimensional plane. Various projection methods and point cloud coloring techniques are employed to enhance SAM’s segmentation performance in images. Finally, the semantic segmentation of the entire underground tunnel is achieved by a small set of manually annotated semantic labels used as prompts in a progressive and recursive manner. The key feature of this method lies in its independence from model training, as it directly and efficiently addresses tunnel point cloud segmentation challenges by capitalizing on the generalization capability of foundation model. Comparative experiments against classical region growing algorithms and PointNet++ deep learning algorithms demonstrate the superior performance of our proposed algorithm.

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“…It mostly adopts the methods of structural equation model, case study and empirical analysis, which focuses on the research on the influencing factors ( Goh, 2006 ), action mechanism ( Bolek and Romanová, 2020 ; Wang et al., 2020 ) and realization path ( Mao and Huang, 2021 ; Lacheheub and Maamri, 2016 ; Chowdary and Kuppili, 2021 ) of intelligent construction. The research on intelligent construction of coal mine is mainly concentrated in the field of engineering technology, focusing on intelligent technology development of coal mine ( Fu et al., 2021 ), system construction ( Agrawal et al., 2017 ), intelligent optimization ( Xing et al., 2021 ; Laub, 1999 ), etc., which lacks the research on intelligent construction of coal mine enterprises from the perspective of management. The problem of promoting the intelligent construction of enterprises is actually manifested in the game process of strategy interaction of relevant stakeholders.…”

Section: Literature Reviewmentioning

confidence: 99%

Evolution dynamic of intelligent construction strategy of coal mine enterprises in China

Yang

et al. 2022

Heliyon

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

Cited by 7 publications

References 53 publications

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

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

A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model

Evolution dynamic of intelligent construction strategy of coal mine enterprises in China

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