Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Zhao, Shuanfeng; Guo, Wei; Guo, Xiaojun; Wang, Yuan

doi:10.3390/ijgi10070482

Cited by 22 publications

(11 citation statements)

References 51 publications

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“…The contact method is usually installed on a single weighing device on a long conveyor belt frame, and the structure includes a weighing frame, a load cell, a speed sensor, and a totalizer meter. The advantages of this method include low-cost, simple and convenient installation, and no need to make too many changes to the original equipment infrastructure [10][11][12][13]. Scholars have used the discrete element method (DEM) to study the interaction between scraper conveyors and coal flow [14].…”

Section: Literature Reviewmentioning

confidence: 99%

A Scraper Conveyor Coal Flow Monitoring Method Based on Speckle Structured Light Data

et al. 2022

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Aiming at the problem of serious shutdowns of conveyors caused by abnormal coal flow of scraper conveyors, a coal flow monitoring method based on speckle structured light is proposed. The point cloud data of the coal body on the scraper conveyor is collected through the speckle structured light acquisition system. Then, the proposed PDS-Algorithm (Planar Density Simplification Algorithm) is used to complete the simplification and differentiation of the collected point cloud data, which provides a basis for constructing geometric characteristics of coal flow lineament. This paper uses the processed point cloud data to calculate the volume of the coal mass and monitor the coal flow of the scraper conveyor. Finally, this method is used in the detection of abnormal coal flow of a coal mine scraper conveyor, and the results show that the proposed abnormal flow monitoring method can meet the accuracy and real-time requirements of coal mine abnormal alarms.

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Section: Literature Reviewmentioning

confidence: 99%

A Scraper Conveyor Coal Flow Monitoring Method Based on Speckle Structured Light Data

et al. 2022

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“…Extracting information about the vehicle's driving status requires converting the pixel coordinates in the image to world coordinates and calibrating the surveillance camera. Xing et al [25] used dynamic map convolutional neural networks to obtain the position of the target in the point cloud for coordinate transformation under real working conditions. Camera calibration often uses self-calibration techniques based on the extended Kalman filter and the fundamental matrix [26,27].…”

Section: Coordinate Conversionmentioning

confidence: 99%

Methodological Study on the Influence of Truck Driving State on the Accuracy of Weigh-in-Motion System

et al. 2022

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The weigh-in-motion (WIM) system weighs the entire vehicle by identifying the dynamic forces of each axle of the vehicle on the road. The load of each axle is very important to detect the total weight of the vehicle. Different drivers have different driving behaviors, and when large trucks pass through the weighing detection area, the driving state of the trucks may affect the weighing accuracy of the system. This paper proposes YOLOv3 network model as the basis for this algorithm, which uses the feature pyramid network (FPN) idea to achieve multi-scale prediction and the deep residual network (ResNet) idea to extract image features, so as to achieve a balance between detection speed and detection accuracy. In the paper, spatial pyramid pooling (SPP) network and cross stage partial (CSP) network are added to the original network model to improve the learning ability of the convolutional neural network and make the original network more lightweight. Then the detection-based target tracking method with Kalman filtering + RTS (rauch–tung–striebel) smoothing is used to extract the truck driving status information (vehicle trajectory and speed). Finally, the effective size of the vehicle in different driving states on the weighing accuracy is statistically analyzed. The experimental results show that the method has high accuracy and real-time performance in truck driving state extraction, can be used to analyze the influence of weighing accuracy, and provides theoretical support for personalized accuracy correction of WIM system. At the same time, it is beneficial for WIM system to assist the existing traffic system more accurately and provide a highway health management and effective decision making by providing reliable monitoring data.

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“…At the same time, the research team of the author has installed a special track to run the inspection robot outside the scraper conveyor and has obtained the point cloud of the coal mine working face by using the light detection and ranging (LiDAR) on the inspection robot. According to the author’s previous research results, the markers in the coal mine working face can be found through DGCNNs, and then, the point cloud coordinate of the coal mine working face can be converted to the geodetic coordinate system . However, further research still needs to be carried out.…”

Section: Introductionmentioning

confidence: 99%

“…According to the author's previous research results, the markers in the coal mine working face can be found through DGCNNs, and then, the point cloud coordinate of the coal mine working face can be converted to the geodetic coordinate system. 48 However, further research still needs to be carried out. Whether DGCNNs can segment the coal wall and the roof and accurately obtain the intersection line of the coal wall and roof has not been known, and whether DGCNNs can achieve a good application effect in the field of energy and geology has also not been known.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Guo

et al. 2021

ACS Omega

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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.

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Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Cited by 22 publications

References 51 publications

A Scraper Conveyor Coal Flow Monitoring Method Based on Speckle Structured Light Data

A Scraper Conveyor Coal Flow Monitoring Method Based on Speckle Structured Light Data

Methodological Study on the Influence of Truck Driving State on the Accuracy of Weigh-in-Motion System

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

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