Volume 48 Issue 4
Apr.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2022  >  48(4): 14-19, 30
JIN Shukai, WEI Guannan, WANG Chunming, et al. Intelligent identification method for mine car load in coal mine auxiliary shaft[J]. Journal of Mine Automation，2022，48(4)：14-19, 30.  doi: 10.13272/j.issn.1671-251x.2021110055
Citation: JIN Shukai, WEI Guannan, WANG Chunming, et al. Intelligent identification method for mine car load in coal mine auxiliary shaft[J]. Journal of Mine Automation，2022，48(4)：14-19, 30.  doi: 10.13272/j.issn.1671-251x.2021110055
shu

Intelligent identification method for mine car load in coal mine auxiliary shaft

doi: 10.13272/j.issn.1671-251x.2021110055
  • 1.

    School of Mechanical Electronic and Information Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China

  • 2.

    Linyi Mining Group Heze Coal Power Co., Ltd., Heze 274700, China

  • 3.

    Guotun Coal Mine, Linyi Mining Group Heze Coal Power Co., Ltd., Heze 274700, China

  • 4.

    Key Laboratory of Coal Mine Intelligence and Robot Innovative Application, Ministry of Emergency Management, China University of Mining and Technology(Beijing), Beijing 100083, China

  • Received Date: 2021-11-22
  • Rev Recd Date: 2022-03-29
    • Available Online: 2022-03-08
    Abstract
  • The automatic classification of mine car load based on convolution neural network in coal mine auxiliary shaft is realized in practical application. The misdetection and false alarm are caused by simple trigger conditions. Non-mine car objects passing through the detection area can cause the misoperation of driver controlled switch. In order to solve this problem, an intelligent identification method for mine car load in coal mine auxiliary shaft based on target detection model is proposed. An industrial camera is installed at the wellhead of a coal mine auxiliary shaft to collect the images of mine car load and the images are manually labeled so as to construct a mine car identification data set. And the identification accuracy and the real-time performance of three target detection models, namely Faster R-CNN, YOLOv4 and SSD are evaluated. According to the evaluation results, it is concluded that the YOLOv4 model is more suitable for the identification task of mine car load. In order to reduce the model size and improve the identification speed, the YOLOv4 model is improved. The lightweight network MobileNet is used to replace the original backbone characteristic extraction network CSPDarknet53. So the MobileNetv3-YOLOv4 model is constructed. The test results show that the mean average precision(mAP) of the MobileNetv3-YOLOv4 model is 95.03%, and the identification speed is 44 frames/s, which is 0.77% and 27 frames/s higher than that of the YOLOv4 model respectively. In order to facilitate field application and deployment and improve the performance of the mine car load identification model on the embedded platform, a model acceleration method based on inter-layer fusion and model quantization is proposed. The MobileNetv3-YOLOv4 model before and after the acceleration is transplanted to Jetson TX2 for field test of mine car load identification. The results show that the identification speed is increased from 18.3 frames/s before the acceleration of the MobileNetv3-YOLOv4 model to 35.42 frames/s, and the mAP is 94.68%, which meets the real-time and precise detection requirements in the field. And the detection task is only started when the mine car passes the detection area, which avoids the misoperation of driver controlled switch caused by other objects.

     

    • FullText(HTML)
  • loading
    • References(16)
  • [1]
    韩安,陈晓晶,贺耀宜,等. 智能矿山综合管控平台建设构思[J]. 工矿自动化,2021,47(8):7-14.

    HAN An,CHEN Xiaojing,HE Yaoyi,et al. Construction conception of intelligent mine integrated management and control platform[J]. Industry and Mine Automation,2021,47(8):7-14.
    [2]
    王国法,任怀伟,赵国瑞,等. 煤矿智能化十大“痛点”解析及对策[J]. 工矿自动化,2021,47(6):1-11.

    WANG Guofa,REN Huaiwei,ZHAO Guorui,et al. Analysis and countermeasures of ten 'pain points' of intelligent coal mine[J]. Industry and Mine Automation,2021,47(6):1-11.
    [3]
    李慧. 副井井口自动化运输系统研究[J]. 工矿自动化,2021,47(6):124-127,132.

    LI Hui. Research on automatic transport system in auxilary shaft pithead[J]. Industry and Mine Automation,2021,47(6):124-127,132.
    [4]
    宋庆军, 姜海燕, 王忠勇, 等. 一种矿车运输智能煤矸识别及分运装置: CN201610922689.2[P]. 2019-08-13.

    SONG Qingjun, JIANG Haiyan, WANG Zhongyong, et al. An intelligent coal gangue identification and separation device for mine car transportation: CN201610922689.2[P]. 2019-08-13.
    [5]
    袁源,汪嘉文,朱德昇,等. 顶煤放落过程煤矸声信号特征提取与分类方法[J]. 矿业科学学报,2021,6(6):711-720.

    YUAN Yuan,WANG Jiawen,ZHU Desheng,et al. Feature extraction and classification method of coal gangue acoustic signal during top coal caving[J]. Journal of Mining Science and Technology,2021,6(6):711-720.
    [6]
    饶中钰,吴景涛,李明. 煤矸石图像分类方法[J]. 工矿自动化,2020,46(3):69-73.

    RAO Zhongyu,WU Jingtao,LI Ming. Coal-gangue image classification method[J]. Industry and Mine Automation,2020,46(3):69-73.
    [7]
    KIDO S, HIRANO Y, HASHIMOTO N. Detection and classification of lung abnormalities by use of convolutional neural network（ CNN） and regions with CNN features （R-CNN）[C]//International Workshop on Advanced Image Technology, Chiang Mai, 2018: 1-4.
    [8]
    刘彪, 郭翔, 张帆, 等. 基于卷积神经网络的煤矿副井矿车装载物自动分类系统[C]//第32届中国控制与决策会议论文集, 合肥, 2020: 580-585.

    LIU Biao, GUO Xiang, ZHANG Fan, et al. Automatic classification system of auxiliary mine car load based on convolutional neural network[C]//Proceedings of the 32nd Chinese Control and Decision Conference, Hefei, 2020: 580-585.
    [9]
    ROH M C, LEE J Y. Refining faster-RCNN for accurate object detection[C]//The 15th International Conference on Machine Vision Applications, Nagoya, 2017: 514-517.
    [10]
    BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[Z/OL]. arvPrint, arXiv: 2004.10934. https://arxiv.org/abs/2004.10934.
    [11]
    张帆,栾佳星,崔东林,等. 基于SSD−LeNet的矿井移动目标检测与识别方法[J]. 矿业科学学报,2021,6(1):100-108.

    ZHANG Fan,LUAN Jiaxing,CUI Donglin,et al. SSD-LeNet based method of mine moving target detection and recognition[J]. Journal of Mining Science and Technology,2021,6(1):100-108.
    [12]
    PANG Shanchen,WANG Shuo,RODRIGUEZ-PATON A,et al. An artificial intelligent diagnostic system on mobile Android terminals for cholelithiasis by lightweight convolutional neural network[J]. PLoS One,2019,14(9):e0221720. doi: 10.1371/journal.pone.0221720
    [13]
    陈智超,焦海宁,杨杰,等. 基于改进MobileNet v2的垃圾图像分类算法[J]. 浙江大学学报(工学版),2021,55(8):1490-1499.

    CHEN Zhichao,JIAO Haining,YANG Jie,et al. Garbage image classification algorithm based on improved MobileNet v2[J]. Journal of Zhejiang University(Engineering Science),2021,55(8):1490-1499.
    [14]
    邵伟平,王兴,曹昭睿,等. 基于MobileNet与YOLOv3的轻量化卷积神经网络设计[J]. 计算机应用,2020,40(增刊1):8-13.

    SHAO Weiping,WANG Xing,CAO Zhaorui,et al. Design of lightweight convolutional neural network based on MobileNet and YOLOv3[J]. Journal of Computer Applications,2020,40(S1):8-13.
    [15]
    MA C,MU X,SHA D. Multi-Layers feature fusion of convolutional neural network for scene classification of remote sensing[J]. IEEE Access,2019,7:121685-121694. doi: 10.1109/ACCESS.2019.2936215
    [16]
    BAO Jian, ZHOU Bin. Optimization of neural network with fixed-point weights and touch-screen calibration[C]//The 4th IEEE Conference on Industrial Electronics and Applications, Xi'an, 2009: 3704-3708.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(5)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (290) PDF downloads(38) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return