Volume 50 Issue 2
Feb.  2024
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QIN Yulong, CHENG Jiming, REN Yige, et al. Large coal detection for belt conveyors based on improved YOLOv5[J]. Journal of Mine Automation,2024,50(2):57-62, 71.  doi: 10.13272/j.issn.1671-251x.2023080096
Citation: QIN Yulong, CHENG Jiming, REN Yige, et al. Large coal detection for belt conveyors based on improved YOLOv5[J]. Journal of Mine Automation,2024,50(2):57-62, 71.  doi: 10.13272/j.issn.1671-251x.2023080096

Large coal detection for belt conveyors based on improved YOLOv5

doi: 10.13272/j.issn.1671-251x.2023080096
  • Received Date: 2023-08-27
  • Rev Recd Date: 2024-02-21
  • Available Online: 2024-03-05
  • Oversized coal blocks can easily cause poor coal flow, blockage, and coal stacking when transported on a belt conveyor. However, the differences in appearance and color between large coal blocks and ordinary coal blocks are small, and there are obstructions and stacking between coal blocks. Existing coal block detection methods are not precise enough to distinguish between large coal blocks and ordinary coal blocks, which can easily lead to missed or false detections. In order to solve the above problems, a modified YOLOv5 model is proposed for detecting large coal blocks in belt conveyors. The model uses parallel dilated convolution modules to replace some ordinary convolution modules in the YOLOv5 backbone network. It expands the receptive field, improves multi-scale feature learning capability, and better distinguishes large coal blocks from ordinary coal blocks. The joint attention module is added to the neck network to better integrate contextual information and improve the positioning capability for large coal blocks. The model uses the trained improved YOLOv5 model to detect real-time coal transportation videos captured by the camera, and links PLC alarms in real-time based on the quantity information of large coal blocks. The experimental results show that compared to the original YOLOv5 model, the improved YOLOv5 model has improved recall and average precision by 3.4% and 2.0%, respectively. PLC can operate corresponding indicator lights and buzzers to alert based on the quantity of large coal blocks detected by the improved YOLOv5 model. The improved YOLOv5 model is applied to actual coal transportation videos in coal mines, with a detection precision of 97.0% for large coal blocks, effectively avoiding missed and false detections.

     

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  • [1]
    WANG Yuan,GUO Wei,ZHAO Shuanfeng,et al. A big coal block alarm detection method for scraper conveyor based on YOLO-BS[J]. Sensors,2022,22(23). DOI: 10.3390/s22239052.
    [2]
    黄燕,胡俊. 一种煤炭智能检测辅助装置的研究与设计[J]. 中国检验检测,2023,31(2):23-24.

    HUANG Yan,HU Jun. Research and design of an intelligent auxiliary device for coal detection[J]. China Inspection Body & Laboratory,2023,31(2):23-24.
    [3]
    张渤,谢金辰,张后斌. 矿井下输送带大块物体检测[J]. 煤炭技术,2021,40(4):154-156.

    ZHANG Bo,XIE Jinchen,ZHANG Houbin. Detection of large objects in transportation belt under mine[J]. Coal Technology, 2021,40(4):154-156.
    [4]
    王卫东,张康辉,吕子奇,等. 基于深度学习的煤中异物机器视觉检测[J]. 矿业科学学报,2021,6(1):115-123.

    WANG Weidong,ZHANG Kanghui,LYU Ziqi,et al. Machine vision detection of foreign objects in coal using deep learning[J]. Journal of Mining Science and Technology,2021,6(1):115-123.
    [5]
    REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once:unified,real-time object detection[C]. IEEE Conference on Computer Vision and Pattern Recognition,Las Vegas,2016:779-788.
    [6]
    杜京义,郝乐,王悦阳,等. 一种煤矿井下输煤大块物检测方法[J]. 工矿自动化,2020,46(5):63-68.

    DU Jingyi,HAO Le,WANG Yueyang,et al. A detection method for large blocks in underground coal transportation[J]. Journal of Mine Automation,2020,46(5):63-68.
    [7]
    叶鸥,窦晓熠,付燕,等. 融合轻量级网络和双重注意力机制的煤块检测方法[J]. 工矿自动化,2021,47(12):75-80.

    YE Ou,DOU Xiaoyi,FU Yan,et al. Coal block detection method integrating lightweight network and dual attention mechanism[J]. Industry and Mine Automation,2021,47(12):75-80.
    [8]
    沈科,季亮,张袁浩,等. 基于改进YOLOv5s模型的煤矸目标检测[J]. 工矿自动化,2021,47(11):107-111,118.

    SHEN Ke,JI Liang,ZHANG Yuanhao,et al. Research on coal and gangue detection algorithm based on improved YOLOv5s model[J]. Industry and Mine Automation,2021,47(11):107-111,118.
    [9]
    张旭辉,闫建星,张超,等. 基于改进YOLOv5s+DeepSORT的煤块行为异常识别[J]. 工矿自动化,2022,48(6):77-86,117.

    ZHANG Xuhui,YAN Jianxing,ZHANG Chao,et al. Coal block abnormal behavior identification based on improved YOLOv5s+DeepSORT[J]. Journal of Mine Automation,2022,48(6):77-86,117.
    [10]
    高凯,董立红,邓凡. 基于递归门控卷积和上下文注意力的煤块检测算法[J]. 矿业研究与开发,2023,43(6):183-190.

    GAO Kai,DONG Lihong,DENG Fan. Coal block detection algorithm based on recursive gated convolution and contextual attention[J]. Mining Research and Development,2023,43(6):183-190.
    [11]
    寇发荣,肖伟,何海洋,等. 基于改进YOLOv5的煤矿井下目标检测研究[J]. 电子与信息学报,2023,45(7):2642-2649.

    KOU Farong,XIAO Wei,HE Haiyang,et al. Research on target detection in underground coal mines based on improved YOLOv5[J]. Journal of Electronics & Information Technology,2023,45(7):2642-2649.
    [12]
    DENG Jun,XUAN Xiaojing,WANG Weifeng,et al. A review of research on object detection based on deep learning[J]. Journal of Physics Conference Series,2020,1684(1). DOI: 10.1088/1742-6596/1684/1/012028.
    [13]
    樊红卫,刘金鹏,曹现刚,等. 低照度尘雾下煤、异物及输送带早期损伤多尺度目标智能检测方法[J/OL]. 煤炭学报:1-12[2023-08-22].https://doi.org/10.13225/j.cnki.jccs.2023.0707.

    FAN Hongwei,LIU Jinpeng,CAO Xiangang,et al. Multi-scale target intelligent detection method for coal,foreign object and early damage of conveyor belt surface under low illumination and dust fog[J/OL]. Journal of China Coal Society:1-12[2023-08-22]. https://doi.org/10.13225/j.cnki.jccs.2023.0707.
    [14]
    卢才武,闫雪颂,刘力,等. 一种改进的无锚框式金属矿带式输送机异物检测方法[J]. 采矿技术,2022,22(1):150-154,162.

    LU Caiwu,YAN Xuesong,LIU Li,et al. An improved foreign object detection method for anchorless frame metal belt conveyor[J]. Mining Technology,2022,22(1):150-154,162.
    [15]
    郭永存,张勇,李飞,等. 嵌入空洞卷积和批归一化模块的智能煤矸识别算法[J]. 矿业安全与环保,2022,49(3):45-50.

    GUO Yongcun,ZHANG Yong,LI Fei,et al. Intelligent coal and gangue identification algorithm embedded in dilated convolution and batch normalization module[J]. Mining Safety & Environmental Protection,2022,49(3):45-50.
    [16]
    WANG Qilong,WU Banggu,ZHU Pengfei,et al. ECA-Net:efficient channel attention for deep convolutional neural networks[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition,Seattle,2020:11531-11539.
    [17]
    何乐,李忠伟,罗偲,等. 基于空洞卷积与双注意力机制的红外与可见光图像融合[J]. 红外技术,2023,45(7):732-738.

    HE Le,LI Zhongwei,LUO Cai,et al. Infrared and visible image fusion based on dilated convolution and dual attention mechanism[J]. Infrared Technology,2023,45(7):732-738.
    [18]
    汤翔中,高丙朋. 融合注意力空洞卷积和Transformer的矿石图像分割[J]. 科学技术与工程,2023,23(16):6974-6982. doi: 10.12404/j.issn.1671-1815.2023.23.16.06974

    TANG Xiangzhong,GAO Bingpeng. Ore image segmentation based on attention hole convolution and transformer[J]. Science Technology and Engineering,2023,23(16):6974-6982. doi: 10.12404/j.issn.1671-1815.2023.23.16.06974
    [19]
    王渊,郭卫,张传伟,等. 融合注意力机制和先验知识的刮板输送机异常煤块检测[J]. 西安科技大学学报,2023,43(1):192-200.

    WANG Yuan,GUO Wei,ZHANG Chuanwei,et al. Detection of abnormal coal block in scraper conveyor integrating attention mechanism and prior knowledge[J]. Journal of Xi'an University of Science and Technology,2023,43(1):192-200.
    [20]
    VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]. The 31st International Conference on Neural Information Processing Systems,Long Beach,2017:6000-6010.
    [21]
    REN Shaoqing,HE Kaiming,GIRSHICK R,et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149. doi: 10.1109/TPAMI.2016.2577031
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