MAO Qinghua, LI Shikun, HU Xin, et al. Foreign object recognition of belt conveyor in coal mine based on improved YOLOv7[J]. Journal of Mine Automation,2022,48(12):26-32. DOI: 10.13272/j.issn.1671-251x.2022100011
Citation: MAO Qinghua, LI Shikun, HU Xin, et al. Foreign object recognition of belt conveyor in coal mine based on improved YOLOv7[J]. Journal of Mine Automation,2022,48(12):26-32. DOI: 10.13272/j.issn.1671-251x.2022100011

Foreign object recognition of belt conveyor in coal mine based on improved YOLOv7

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  • Received Date: October 07, 2022
  • Revised Date: December 17, 2022
  • Available Online: December 04, 2022
  • The coal flow of the belt conveyor will be mixed with anchor rod, angle iron, wood, gangue, and lump coal. This will easily lead to the tearing of the conveyor belt, the blockage of the transition and even the breakage of the belt. It is difficult for the inspection robot of the belt conveyor to efficiently and accurately recognize foreign objects in the environment of uneven lighting and high-speed running of the belt conveyor. The model deployment is inconvenient. The YOLOv7 model has a high capability to extract target features, but its recognition speed is slow. In order to solve the above problems, a foreign object recognition method of belt conveyor in coal mine based on improved YOLOv7 is proposed. The method of adaptive histogram equalization with limited contrast is used to enhance the collected monitoring image of the belt conveyor to improve the clarity of object contour in the image. The YOLOv7 model is improved by introducing a simple and parameter-free attention module into the backbone extraction network. The improved model can improve the model's anti-interference capability against the complex background of the image and the capability to extract foreign object features. The depthwise separable convolution is introduced to replace the ordinary convolution in the backbone feature extraction network to improve the speed of foreign object recognition. TensorRT engine is used to convert the improved YOLOv7 model after training and deploy it on NVIDIA Jetson Xavier NX, realizing the acceleration of the model. The video of the belt conveyor with the resolution of 1 920 × 1 080 in the underground coal mine is recognized. The experimental results show that the recognition effect of improved YOLOv7 is better than YOLOv5L and YOLOv7. The recognition accuracy rate is 92.8%, and the recognition speed is 25.64 frames/s, meeting the requirements of accurate and efficient recognition of foreign objects in the belt conveyor.
  • [1]
    葛世荣,胡而已,裴文良. 煤矿机器人体系及关键技术[J]. 煤炭学报,2020,45(1):455-463. DOI: 10.13225/j.cnki.jccs.YG19.1478

    GE Shirong,HU Eryi,PEI Wenliang. Classification system and key technology of coal mine robot[J]. Journal of China Coal Society,2020,45(1):455-463. DOI: 10.13225/j.cnki.jccs.YG19.1478
    [2]
    方崇全. 煤矿带式输送机巡检机器人关键技术研究[J]. 煤炭科学技术,2022,50(5):263-270. DOI: 10.13199/j.cnki.cst.ZN20-056

    FANG Chongquan. Research on key technology of inspection robot for coal mine belt conveyor[J]. Coal Science and Technology,2022,50(5):263-270. DOI: 10.13199/j.cnki.cst.ZN20-056
    [3]
    吴守鹏,丁恩杰,俞啸. 基于改进FPN的输送带异物识别方法[J]. 煤矿安全,2019,50(12):127-130. DOI: 10.13347/j.cnki.mkaq.2019.12.029

    WU Shoupeng,DING Enjie,YU Xiao. Foreign body identification of belt based on improved FPN[J]. Safety in Coal Mines,2019,50(12):127-130. DOI: 10.13347/j.cnki.mkaq.2019.12.029
    [4]
    吕志强. 复杂环境下煤矿皮带运输异物图像识别研究[D]. 徐州: 中国矿业大学, 2020: 1-60.

    LYU Zhiqiang. Research on foreign body image recognition of coal mine belt transport under complex environment[D]. Xuzhou: China University of Mining and Technology, 2020: 1-60.
    [5]
    任志玲, 朱彦存. 改进CenterNet算法的煤矿皮带运输异物识别研究[J/OL]. 控制工程: 1-8[2022-09-28]. DOI: 10.14107/j. cnki. kzgc. 20200792.

    REN Zhiling, ZHU Yancun. Research on foreign objects recognition of coal mine belt transportation with improved CenterNet algorithm[J/OL]. Control Engineering of China: 1-8[2022-09-28]. DOI: 10.14107/j.cnki.kzgc.20200792.
    [6]
    胡璟皓,高妍,张红娟,等. 基于深度学习的带式输送机非煤异物识别方法[J]. 工矿自动化,2021,47(6):57-62,90. DOI: 10.13272/j.issn.1671-251x.2021020041

    HU Jinghao,GAO Yan,ZHANG Hongjuan,et al. Research on the identification method of non-coal foreign object of belt conveyor based on deep learning[J]. Industry and Mine Automation,2021,47(6):57-62,90. DOI: 10.13272/j.issn.1671-251x.2021020041
    [7]
    WANG Yuanbin,WANG Yujing,DANG Langfei. Video detection of foreign objects on the surface of belt conveyor underground coal mine based on improved SSD[J]. Journal of Ambient Intelligence and Humanized Computing,2020:1-10.
    [8]
    郝帅,张旭,马旭,等. 基于CBAM−YOLOv5的煤矿输送带异物检测[J]. 煤炭学报,2022,47(11):4147-4156. DOI: 10.13225/j.cnki.jccs.2021.1644

    HAO Shuai,ZHANG Xu,MA Xu,et al. Foreign object detection in coal mine conveyor belt based on CBAM-YOLOv5[J]. Journal of China Coal Society,2022,47(11):4147-4156. DOI: 10.13225/j.cnki.jccs.2021.1644
    [9]
    程德强,徐进洋,寇旗旗,等. 融合残差信息轻量级网络的运煤皮带异物分类[J]. 煤炭学报,2022,47(3):1361-1369. DOI: 10.13225/j.cnki.jccs.xr21.1736

    CHENG Deqiang,XU Jinyang,KOU Qiqi,et al. Lightweight network based on residual information for foreign body classification on coal conveyor belt[J]. Journal of China Coal Society,2022,47(3):1361-1369. DOI: 10.13225/j.cnki.jccs.xr21.1736
    [10]
    XIAO Dong,KANG Zhuang,YU Hang,et al. Research on belt foreign body detection method based on deep learning[J]. Transactions of the Institute of Measurement and Control,2022,44(15):2919-2927. DOI: 10.1177/01423312221094393
    [11]
    WANG C Y, BOCHKOVSKIY A, LIAO H. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J/OL]. [2022-09-28]. https://arxiv.org/abs/2207.02696.
    [12]
    杨骥,杨亚东,梅雪,等. 基于改进的限制对比度自适应直方图的视频快速去雾算法[J]. 计算机工程与设计,2015,36(1):221-226. DOI: 10.16208/j.issn1000-7024.2015.01.040

    YANG Ji,YANG Yadong,MEI Xue,et al. Fast video dehazing based on improved contrast limited adaptive histogram equalization[J]. Computer Engineering and Design,2015,36(1):221-226. DOI: 10.16208/j.issn1000-7024.2015.01.040
    [13]
    舒甜督. 医学CT图像的增强与分类算法研究[D]. 长春: 长春工业大学, 2022.

    SHU Tiandu. Research on enhancement and classification algorithm of medical CT images[D]. Changchun: Changchun University of Technology, 2022.
    [14]
    QIN Xiaoyi, LI Na, WENG Chao, et al. Simple attention module based speaker verification with iterative noisy label detection[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, 2021.
    [15]
    CHOLLET F. Xception: Deep learning with depthwise separable convolutions[C]. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017.
    [16]
    顾德英,罗聿伦,李文超. 基于改进YOLOv5算法的复杂场景交通目标检测[J]. 东北大学学报(自然科学版),2022,43(8):1073-1079.

    GU Deying,LUO Yulun,LI Wenchao. Traffic target detection in complex scenes based on improved YOLOv5 algorithm[J]. Journal of Northeastern University(Natural Science),2022,43(8):1073-1079.
    [17]
    MAO Qinghua,WANG Yufei,ZHANG Xuhui,et al. Clarity method of fog and dust image in fully mechanized mining face[J]. Machine Vision and Applications,2022,33(2):1-16.
    [18]
    LI Kexin,QIN Liang,LI Qiang,et al. Improved edge lightweight YOLOv4 and its application in on-site power system work[J]. Global Energy Interconnection,2022,5(2):168-180. DOI: 10.1016/j.gloei.2022.04.014
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