LI Yao, WANG Yiha. Adaptive coal flow detection method of belt conveyor[J]. Journal of Mine Automation, 2020, 46(6): 98-102. DOI: 10.13272/j.issn.1671-251x.2019090087
Citation: LI Yao, WANG Yiha. Adaptive coal flow detection method of belt conveyor[J]. Journal of Mine Automation, 2020, 46(6): 98-102. DOI: 10.13272/j.issn.1671-251x.2019090087

Adaptive coal flow detection method of belt conveyor

More Information
  • For problems of existing coal flow detection methods of belt conveyor such as susceptibility of detection accuracy to environment, complex realization process, long time-consumption of information extraction and so on, an adaptive coal flow detection method of belt conveyor based on machine vision was proposed. Firstly, the original coal transportation image of belt conveyor is enhanced by a fusion algorithm based on wavelet transform and segmented by OTSU algorithm into belt image and coal image. Secondly, the segmented coal image is processed by cavity filling, contour detection and area calculation to obtain area information of the coal image. Finally, a coal flow detection algorithm based on mathematical modeling is used to obtain coal flow detection value through calculating transient volume of coal. The test results show that the average detection time of the method is about 30 ms, and error between detection results and the measurement ones of electronic belt scale is about 5%, which meets real-time and accuracy requirements for coal flow detection of automatic speed control system of belt conveyor.
  • Related Articles

    [1]LI Ji, MA Xiaofeng, WU Jieqi, QIANG Xubo, WU Liyang, YAN Bo, DONG Jihui, CHEN Chaosen. Coal-rock image recognition method integrating drilling geological information[J]. Journal of Mine Automation, 2024, 50(8): 38-43, 68. DOI: 10.13272/j.issn.1671-251x.2024040048
    [2]HAO Bonan. Coal mine underground image enhancement method based on dust removal estimation and multiple exposure fusion[J]. Journal of Mine Automation, 2023, 49(11): 100-106. DOI: 10.13272/j.issn.1671-251x.2023080105
    [3]FENG Wei, YAO Wanqiang, LIN Xiaohu, ZHENG Junliang, XIANGLI Hailong, XUE Zhiqiang. Visual simultaneous localization and mapping algorithm of coal mine underground considering image enhancement[J]. Journal of Mine Automation, 2023, 49(5): 74-81. DOI: 10.13272/j.issn.1671-251x.2022090025
    [4]LI Zhenglong, WANG Hongwei, CAO Wenyan, ZHANG Fujing, WANG Yuheng. A method for enhancing low light images in coal mines based on Retinex model containing noise[J]. Journal of Mine Automation, 2023, 49(4): 70-77. DOI: 10.13272/j.issn.1671-251x.2022080047
    [5]KONG Erwei, ZHANG Yabang, LI Jiayue, WANG Manli. An enhancement method for low light images in coal mines[J]. Journal of Mine Automation, 2023, 49(4): 62-69, 85. DOI: 10.13272/j.issn.1671-251x.2022110054
    [6]ZUO Chunzi, WANG Zheng, ZHANG Ke, PAN Hongguang. Coal dust image segmentation method based on improved DeepLabV3+[J]. Journal of Mine Automation, 2022, 48(5): 52-57, 64. DOI: 10.13272/j.issn.1671-251x.2021120086
    [7]WANG Hongdong, GUO Weidong, ZHU Meiqiang, LEI Meng. An enhancement algorithm for low-illumination image of underground coal mine[J]. Journal of Mine Automation, 2019, 45(11): 81-85. DOI: 10.13272/j.issn.1671-251x.17498
    [8]CHENG Deqiang, ZHENG Zhen, JIANG Hailong. An image enhancement algorithm for coal mine underground[J]. Journal of Mine Automation, 2015, 41(12): 31-34. DOI: 10.13272/j.issn.1671-251x.2015.12.009
    [9]WANG Xiaobing, YAO Xueqing, QIU Yinguo, SUN Jiuyun. A new filtering algorithm for video monitoring image of coal mine[J]. Journal of Mine Automation, 2014, 40(11): 76-80. DOI: 10.13272/j.issn.1671-251x.2014.11.018
    [10]YING Dong-jie, LI Wen-jie. Analysis of Enhancement Algorithms of Coal Mine Monitoring Image and Its Realizatio[J]. Journal of Mine Automation, 2012, 38(8): 55-58.

Catalog

    Article Metrics

    Article views (260) PDF downloads (30) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return