Volume 48 Issue 6
Jun.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2022  >  48(6): 128-133
GUI Fangjun, LI Yao. Coal gangue detection based on CBA-YOLO model[J]. Journal of Mine Automation，2022，48(6)：128-133.  doi: 10.13272/j.issn.1671-251x.2022020033
Citation: GUI Fangjun, LI Yao. Coal gangue detection based on CBA-YOLO model[J]. Journal of Mine Automation，2022，48(6)：128-133.  doi: 10.13272/j.issn.1671-251x.2022020033
shu

Coal gangue detection based on CBA-YOLO model

doi: 10.13272/j.issn.1671-251x.2022020033

  • School of Electromechanical and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

  • Received Date: 2022-02-19
  • Rev Recd Date: 2022-06-03
    • Available Online: 2022-03-28
    Abstract
  • There are some problems in coal gangue detection, such as small differences of characteristics between samples and dense targets. This leads to low precision and poor real-time performance of the existing coal gangue detection methods. In order to solve this problem, a method of coal gangue detection based on CBA-YOLO model is proposed. The CBA-YOLO model is based on YOLOv5m, which has faster speed and higher precision. The convolutional block attention module (CBAM) is added to the Backbone of YOLOv5m. The spatial attention module and the channel attention module are connected in series to focus on the difference of characteristics and reduce the data dimension. And the detection performance of coal gangue is improved. In the Neck part, the bi-directional feature pyramid network (BiFPN) structure is adopted to improve the calculation efficiency of the model by integrating the features of different scales. Therefore, the detection speed of coal gangue is improved. In the Prediction part, the Alpha-IoU function is used as the loss function. And the weight coefficient is set to accelerate the learning of high confidence targets, so as to further improve the detection precision of coal gangue. The experimental results show that the average detection precision of CBA-YOLO model for coal gangue is 98.2%, which is 3.4% higher than that of YOLOv5 model. The detection speed is increased by 10%. CBA-YOLO model is more robust and can effectively avoid missed detection, false detection and overlap.

     

    • FullText(HTML)
  • loading
    • References(19)
  • [1]
    张振红. 我国干法选煤技术发展现状与应用前景[J]. 选煤技术,2019(1):43-47,52.

    ZHANG Zhenhong. China's coal dry cleaning technology−state-of-the-art and application prospect[J]. Coal Preparation Technology,2019(1):43-47,52.
    [2]
    SU Lingling, CAO Xiangang, MA Hongwei, et al. Research on coal gangue identification by using convolutional neural network[C]//IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, Xi'an, 2018: 810-814.
    [3]
    HONG Huichao, ZHENG Lixin, ZHU Jianqing, et al. Automatic recognition of coal and gangue based on convolution neural network[EB/OL]. （2017-12-03）[2022-01-05]. https://arxiv.org/abs/1712.00720.
    [4]
    赵明辉. 一种煤矸石优化识别方法[J]. 工矿自动化,2020,46(7):113-116.

    ZHAO Minghui. A coal-gangue optimization identification method[J]. Industry and Mine Automation,2020,46(7):113-116.
    [5]
    GIRSHICK R. Fast R-CNN[C]//IEEE International Conference on Computer Vision, Chile, 2015.
    [6]
    LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]// European Conference on Computer Vision, 2016: 21-37.
    [7]
    REDMON J, FARHADI A. Yolov3: an incremental improvement[EB/OL]. （2018-04-08） [2022-01-05]. https://arxiv.org/abs/1804.02767.
    [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]
    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.
    [10]
    BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. （2020-04-23） [2022-01-05]. https://arxiv.org/abs/2004.10934.
    [11]
    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,2020,1684:012028. doi: 10.1088/1742-6596/1684/1/012028
    [12]
    HU Jie,SHEN Li,ALBANIE S,et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(8):2011-2023. doi: 10.1109/TPAMI.2019.2913372
    [13]
    LI Xiang, WANG Wenhai, HU Xiaolin, et al. Selective kernel networks[EB/OL]. （2019-05-18）[2022-01-06]. https://arxiv.org/abs/1903.06586.
    [14]
    WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[EB/OL]. （2018-07-18）[2022-01-06]. https://arxiv.org/abs/1807.06521.
    [15]
    TAN M, PANG R, LE Q V. Efficientdet: scalable and efficient object detection[EB/OL]. （2020-07-27）[2022-01-06]. https://arxiv.org/abs/1911.09070.
    [16]
    JIANG Borui, LUO Ruixuan, MAO Jiayuan, et al. Acquisition of localization confidence for accurate object detection[C]// European Conference on Computer Vision, 2018: 816-832.
    [17]
    REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union: a metric and a loss for bounding box regression [EB/OL]. （2019-04-15）[2022-01-06]. https://arxiv.org/abs/1902.09630.
    [18]
    ZHENG Zhaohui, WANG Ping, REN Dongwei, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[EB/OL]. （2021-07-05）[2022-01-06]. https://arxiv.org/abs/2005.03572.
    [19]
    HE Jiabo, ERFANI S, MA Xingjun, et al. Alpha-IoU: a family of power intersection over union losses for bounding box regression[EB/OL]. （2021-10-26）[2022-01-06]. https://arxiv.org/abs/2110.13675.
    • 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(9)  / Tables(1)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (485) PDF downloads(76) 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