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

Wang, Yuan; Guo, Wei; Zhao, Shuanfeng; Xue, Buqing

doi:10.3390/app12146955

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…However, compared to other visual sensors, the utilization of structured light underground is relatively limited. Existing applications include obstacle avoidance for mining vehicles [30] , pedestrian detection [31] , SLAM [32] , navigation and positioning [8] , parameter measurement [33] , and coal block morphology measurement [34] . Nevertheless, after several decades of development, structured light systems still have certain limitations, particularly in measuring complex surface shapes, highly reflective objects, and real-time measurements of dynamic objects.…”

Section: ) Structured Light Camerasmentioning

confidence: 99%

“…On the basis of 3D point cloud reconstruction of the coal stack and yard in the absolute coordinate system, volume calculation and weight estimation systems were established in [158] based on image processing. [33] used point cloud data collected through a speckle-structured light acquisition system to calculate the volume of the coal mass and monitor the coal flow of the conveyor. Using an industrial camera to collect dynamic images of a conveyor belt irradiated by a laser transmitter, [159] presented a method for coal quantity detection and classification based on machine vision and deep learning.…”

Section: ) Transport Parameters Intelligent Sensingmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Du,

Zhang,

Liang

et al. 2023

IEEE Access

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The realization of intelligent mining is the only method for realizing high-quality development in the coal industry. As the forefront working link in mine production, achieving automatic roadway tunneling control is key to improving production efficiency, enhancing intelligence, and reducing accident rates at fully mechanized tunneling working faces. Among various detection techniques, machine vision technology stands out with advantages of non-contact measurement, rich information acquisition, and high detection accuracy. The detection and control of tunneling equipment groups based on machine vision has become a research hotspot in the intelligence process of coal mines. This study first introduces the key technologies of a visual detection system, including camera calibration, image preprocessing, feature extraction, visual matching, target segmentation and recognition, visual measurement, and 3D reconstruction. It then elaborates on detection principles, workflows, limitations, precautions, and development status of various vision detection systems in practical application scenarios at tunneling faces, such as tunneling equipments, anchoring systems, transportation systems, and safety auxiliary systems, which significantly improve production safety and efficiency. Finally, considering challenging work conditions and strong interference in mines, the successful adaptation of machine vision to excavation sites relies on addressing technical challenges related to poor environment adaptability, limited imaging field of view, and low intelligence level. Furthermore, according to existing research results and the current technical status, this paper forecasts key technologies that need to be developed in the future for coal mine intelligent equipment systems based on machine vision, including multi-sensor information fusion, equipment group collaborative control, and digital twin-driven remote monitoring.INDEX TERMS Machine vision, coal machinery equipment, detection and measurement, roadway fully mechanized tunneling faces, unmanned coal mine.

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Section: ) Structured Light Camerasmentioning

confidence: 99%

Section: ) Transport Parameters Intelligent Sensingmentioning

confidence: 99%

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Du,

Zhang,

Liang

et al. 2023

IEEE Access

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show abstract

“…In recent years, laser detection [5], machine visual detection and other noncontact methods are widely used due to high accuracy and low environmental impact. Yuan Wang [6] proposed PDS-Algorithm (Planar Density Simplification Algorithm) 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 realtime requirements of coal mine abnormal alarms. Guimei Wang [7] presented a method of coal volume detection and classification based on machine vision and deep learning.…”

Section: Introductionmentioning

confidence: 99%

Research on Coal Volume Detection and Energy-Saving Optimization Intelligent Control Method of Belt Conveyor Based on Laser and Binocular Visual Fusion

Wen

Hao

et al. 2024

IEEE Access

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Real-time coal flow monitoring is crucial for efficient coal mine transportation. Traditional vision acquisition devices generate two-dimensional images that are not accurately identified in the complex underground coal mine environment, such as dust, water mist, and low light. In this paper, we propose a conveyor belt coal flow detection method that integrates laser scanning and binocular vision to address this problem. Our proposed method has several advantages over traditional approaches. Firstly, we calibrate the binocular camera using Zhang's calibration method to enhance the accuracy of the system. Secondly, we extract the centerline of the laser stripe using the grayscale center of gravity method, which improves the system's performance in complex environments. Thirdly, we calculate the cross-sectional area of the material accurately using the trapezoidal area accumulation method, and visualize it in two dimensions based on a single frame, while the point cloud data from multiple consecutive frames are visualized in a 3D model at a realistic scale. Finally, we use the continuous multi-frame cross-sectional area to calculate the current conveyor flow, and apply the BP neural network to establish an energy-saving optimization model for the belt conveyor. We also design a PLC fuzzy controller based on fuzzy control algorithms to adjust the belt's operating speed intelligently according to the coal flow size, achieving energy-saving operation and intelligent control of the belt conveyor. Our experiments show that our method can accurately obtain coal volume and control the belt speed of the conveyor in real-time, making it an innovative and practical solution for coal flow detection and energy-saving operation in underground coal mines.INDEX TERMS machine vision; stereo matching; energy-saving optimization; coal volume detection; belt conveyor

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“…Mining scraper conveyors are used to transport initial coal bodies which have been newly mined using shearers, as shown in Figure 1 . Scraper conveyors are widely used in the field of coal mine material transportation because they have long conveying distances and large conveying capacities [ 1 , 2 , 3 , 4 ]. Shearers have high working efficiency when mining big coal blocks and low efficiency when mining small coal blocks.…”

Section: Introductionmentioning

confidence: 99%

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

Wang

Guo

Zhao

et al. 2022

Sensors

Self Cite

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With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors.

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

Cited by 8 publications

References 25 publications

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Research on Coal Volume Detection and Energy-Saving Optimization Intelligent Control Method of Belt Conveyor Based on Laser and Binocular Visual Fusion

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

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