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基于CBA−YOLO模型的煤矸石检测

桂方俊 李尧

桂方俊,李尧. 基于CBA−YOLO模型的煤矸石检测[J]. 工矿自动化,2022,48(6):128-133.  doi: 10.13272/j.issn.1671-251x.2022020033
引用本文: 桂方俊,李尧. 基于CBA−YOLO模型的煤矸石检测[J]. 工矿自动化,2022,48(6):128-133.  doi: 10.13272/j.issn.1671-251x.2022020033
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

基于CBA−YOLO模型的煤矸石检测

doi: 10.13272/j.issn.1671-251x.2022020033
详细信息
    作者简介:

    桂方俊 (1999—),男,安徽安庆人,硕士研究生,研究方向为图像处理,E-mail: sqt2000407124@student.cumtb.edu.cn

  • 中图分类号: TD948.9

Coal gangue detection based on CBA-YOLO model

  • 摘要: 煤矸石检测中存在样本间特征差异小、目标密集等问题,导致现有煤矸石检测方法精度不高且实时性较差。针对该问题,提出了一种基于CBA−YOLO模型的煤矸石检测方法。CBA−YOLO模型以速度较快、精度较高的YOLOv5m为基础模型,在YOLOv5m的Backbone中加入卷积块注意力模块(CBAM),通过串联空间注意力模块和通道注意力模块,在聚焦特征差异的同时降低数据维度,提高煤矸石检测性能;在Neck部分采用双向特征金字塔网络(BiFPN)结构,通过融合不同尺度的特征提高模型计算效率,从而提升煤矸石检测速度;在Prediction部分采用Alpha−IoU函数作为损失函数,通过设置权重系数加速对高置信度目标的学习,进一步提高煤矸石检测精度。实验结果表明:CBA−YOLO模型对煤矸石的平均检测精度达98.2%,比YOLOv5模型提高了3.4%,检测速度提升了10%;CBA−YOLO模型的鲁棒性更强,可有效避免漏检、误检和重叠现象。

     

  • 图  1  YOLOv5模型的性能

    Figure  1.  The performance of YOLOv5 models

    图  2  CBAM结构

    Figure  2.  Structure of CBAM

    图  3  改进Backbone结构

    Figure  3.  Structure of improved Backbone

    图  4  特征网络结构

    Figure  4.  Structure of features network

    图  5  基于CBA−YOLO模型的煤矸石检测流程

    Figure  5.  Flow of coal gangue detection based on CBA-YOLO model

    图  6  图像采集

    Figure  6.  Image acquisition

    图  7  训练损失

    Figure  7.  Training loss

    图  8  消融实验PR曲线

    Figure  8.  PR curves of ablation experiment

    图  9  煤矸石检测结果对比

    Figure  9.  Comparison of coal gangue detection results

    表  1  消融实验结果

    Table  1.   Results of ablation experiment

    模型mAP/%帧率/(帧·s−1模型mAP/%帧率/(帧·s−1
    YOLOv594.830YOLO−CB96.235.2
    YOLO−C95.732.2YOLO−CA97.430.5
    YOLO−B95.534.1YOLO−BA97.532
    YOLO−A96.029.8CBA−YOLO98.233
    下载: 导出CSV
  • [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.
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出版历程
  • 收稿日期:  2022-02-19
  • 修回日期:  2022-06-03
  • 网络出版日期:  2022-03-28

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