Volume 50 Issue 8
Aug.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2024  >  50(8): 120-126
YANG Yang, LI Haixiong, HU Miaolong, et al. Coal and gangue segmentation and recognition method based on YOLOv5-SEDC model[J]. Journal of Mine Automation，2024，50（8）：120-126.  doi: 10.13272/j.issn.1671-251x.2024010078
Citation: YANG Yang, LI Haixiong, HU Miaolong, et al. Coal and gangue segmentation and recognition method based on YOLOv5-SEDC model[J]. Journal of Mine Automation，2024，50（8）：120-126.  doi: 10.13272/j.issn.1671-251x.2024010078
shu

Coal and gangue segmentation and recognition method based on YOLOv5-SEDC model

doi: 10.13272/j.issn.1671-251x.2024010078
  • 1.

    Jingyuan Coal Engineering Survey and Design Co., Ltd., Baiyin 730913, China

  • 2.

    Yulin Energy Bureau, Yulin 719000, China

  • 3.

    Wins Wireless Network Technology Ltd., Jiaxing 314001, China

  • 4.

    College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

  • Received Date: 2024-01-23
  • Rev Recd Date: 2024-08-13
    • Available Online: 2024-08-12
    Abstract
  • The existing coal and gangue segmentation and recognition technology has a large number of parameters, slow classification speed, and low recognition accuracy. The YOLOv5-seg model is prone to losing texture details and grayscale feature information on the image surface during up and down sampling operations, which reduces the efficiency of coal and gangue recognition. The YOLOv5-seg model overly focuses on global features during training, while neglecting the locally significant regions and features that are crucial for coal and gangue recognition. In order to solve the above problems, a coal and gangue segmentation and recognition method based on YOLOv5-SEDC model is proposed. Firstly, the method receives an image containing the shape information of coal and gangue, and uses the backbone network for feature extraction to generate a feature map. The method integrates the SENet module into the YOLOv5-seg model to preserve the texture details and grayscale features of coal and gangue surfaces, avoiding information loss caused by down sampling. The method adopts a dilated convolution strategy with different dilation rates instead of traditional convolution kernels. It not only expands the receptive field of the model, but also effectively reduces the number of model parameters. Finally, the segmentation detection head finely processes the fused features to achieve precise segmentation and recognition of coal and gangue. A coal and gangue image acquisition experimental platform is established at the actual coal and gangue sorting site of Daliuta Coal Mine. The ablation experiment results show that the accuracy of coal and gangue recognition of YOLOv5-SEDC model is improved by an average of 1.3% compared to YOLOv5-seg model. The parameter quantity is reduced by 0.7×106, and the detection speed is increased by 1.4 frames/s. The comparative experimental results show the following points. ① The accuracy of the YOLOv5-SEDC model is improved by 10.7%, 2.7%, 1.9% compared to the YOLOv3-tiny, YOLOv5-seg, and Mask-RCNN models, respectively, reaching 95.8%. ② The recall rate of the YOLOv5-SEDC model has increased by 3.0%, 2.1%, and 0.9% compared to the YOLOv3-tiny, YOLOv5-seg, and Mask-RCNN models, respectively, reaching 89.1%. ③ The mAP of the YOLOv5-SEDC model has increased by 6.4%, 6.3%, and 1.8% compared to the YOLOv3-tiny, YOLOv5-seg, and Mask-RCNN models, respectively, reaching 95.5%. ④ The F1 value of the YOLOv5-SEDC model has increased by 5.2%, 4.2%, 2.1% compared to the YOLOv3-tiny, YOLOv5-seg, and Mask-RCNN models, respectively, reaching 92.2%. ⑤ The detection speed of the YOLOv5-SEDC model is reduced by 1.9, 1.4, and 2.7 frames/s compared to the YOLOv3-tiny, YOLOv5-seg, and Mask-RCNN models, respectively. The visualization results show that the YOLOv5-SEDC model has higher detection accuracy for coal and gangue than the YOLOv5-seg and Mask-RCNN models. It indicates that the YOLOv5-SEDC model has good performance in coal gangue segmentation and recognition.

     

    • FullText(HTML)
  • loading
    • References(23)
  • [1]
    袁亮,张农,阚甲广,等. 我国绿色煤炭资源量概念、模型及预测[J]. 中国矿业大学学报,2018,47(1):1-8.

    YUAN Liang,ZHANG Nong,KAN Jiaguang,et al. The concept,model and reserve forecast of green coal resources in China[J]. Journal of China University of Mining & Technology,2018,47(1):1-8.
    [2]
    钱鸣高,许家林,王家臣. 再论煤炭的科学开采[J]. 煤炭学报,2018,43(1):1-13.

    QIAN Minggao,XU Jialin,WANG Jiachen. Further on the sustainable mining of coal[J]. Journal of China Coal Society,2018,43(1):1-13.
    [3]
    LI Jianping,DU Changlong,BAO Jianwei,et al. Direct-impact of sieving coal and gangue[J]. Mining Science and Technology,2010,20(4):611-614.
    [4]
    DUAN Chenlong,ZHOU Chenyang,DONG Liang,et al. A novel dry beneficiation technology for pyrite recovery from high sulfur gangue[J]. Journal of Cleaner Production,2018,172(3):2475-2484.
    [5]
    MOHANTA S,MEIKAP B C. Influence of mediumparticle size on the separation performance of an air dense medium fluidized bed separator for coal cleaning[J]. Journal of the South African Institute of Mining and Metallurgy,2015,115:761-766.
    [6]
    李思维,常博,刘昆轮,等. 煤炭干法分选的发展与挑战[J]. 洁净煤技术,2021,27(5):32-37.

    LI Siwei,CHANG Bo,LIU Kunlun,et al. Development and challenge of dry coal separation[J]. Clean Coal Technology,2021,27(5):32-37.
    [7]
    曹现刚,李莹,王鹏,等. 煤矸石识别方法研究现状与展望[J]. 工矿自动化,2020,46(1):38-43.

    CAO Xiangang,LI Ying,WANG Peng,et al. Research status of coal-gangue identification method and its prospect[J]. Industry and Mine Automation,2020,46(1):38-43.
    [8]
    MCCOY J T,AURET L. Machine learning applications in minerals processing:a review[J]. Minerals Engineering,2019,132:95-109.
    [9]
    LI Deyong,WANG Guofa ,ZHANG Yong,et al. Coal gangue detection and recognition algorithm based on deformable convolution YOLOv3[J]. IET Image Processing,2022,16(1):134-144.
    [10]
    SONG Qingjun,LIU Zhijiang,JIANG Haiyan. Coal gangue detection method based on improved YOLOv5[C]. International Conference on Big Data,Artificial Intelligence and Internet of Things Engineering,Xi'an,2022. DOI: 10.1109/ICBAIE56435.2022.9985920.
    [11]
    GUI Fangjun,YU Shuo,ZHANG Hailan,et al. Coal gangue recognition algorithm based on improved YOLOv5[C]. 2nd International Conference on Information Technology,Big Data and Artificial Intelligence,Chongqing,2021.DOI: 10.1109/ICIBA52610.2021.9687869.
    [12]
    FU Chengcai,LU Fengli,ZHANG Guoying. Gradient- enhanced waterpixels clustering for coal gangue[J]. International Journal of Coal Preparation and Utilization,2023,43(4):677-690.
    [13]
    LAI Wenhao,HU Feng,KONG Xixi,et al. The study of coal gangue segmentation for location and shape predicts based on multispectral and improved Mask R-CNN[J]. Powder Technology,2022,407. DOI: 10.1016/J.POWTEC.2022.117655.
    [14]
    LYU Ziqi,WANG Weidong,ZHANG Kanghui,et al. A synchronous detection-segmentation method for oversized gangue on a coal preparation plant based on multi-task learning[J]. Minerals Engineering,2022,187. DOI: 10.1016/J.MINENG.2022.107806.
    [15]
    TAGHANAKI S A,ABHISHEK K,COHEN J P,et al. Deep semantic segmentation of natural and medical images:a review[J]. Artificial Intelligence Review,2021,54(1):137-178.
    [16]
    HAO Shijie,ZHOU Yuan,GUO Yanrong. A brief survey on semantic segmentation with deep learning[J]. Neurocomputing,2020,406:302-321.
    [17]
    陈彪,卢兆林,代伟,等. 基于轻量化HPG−YOLOX−S模型的煤矸石图像精准识别[J]. 工矿自动化,2022,48(11):33-38.

    CHEN Biao,LU Zhaolin,DAI Wei,et al. Accurate recognition of coal-gangue image based on lightweight HPG-YOLOX-S model[J]. Journal of Mine Automation,2022,48(11):33-38.
    [18]
    郝俊峰,李玉涛,来博文. 基于YOLOv5−seg的多模型电石检测分割系统[J]. 现代计算机,2023,29(16):1-7,14. doi: 10.3969/j.issn.1007-1423.2023.16.001

    HAO Junfeng,LI Yutao,LAI Bowen. Multi-model calcium carbide detection and segmentationsystem based on YOLOv5−seg[J]. Modern Computer,2023,29(16):1-7,14. doi: 10.3969/j.issn.1007-1423.2023.16.001
    [19]
    许灿辉,史操,陈以农. 基于膨胀卷积网络的端到端文档语义分割[J]. 中南大学学报(英文版),2021,28(6):1765-1774.

    XU Canhui,SHI Cao,CHEN Yinong. End-to-end dilated convolution network for document image semantic segmentation[J]. Journal of Central South University,2021,28(6):1765-1774.
    [20]
    YU Fisher,KOLTUN V. Multi-scale context aggregation by dilated convolutions[C]. International Conference on Learning Representation,Washington,2016. DOI: 10.48550/arXiv.1511.07122.
    [21]
    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.
    [22]
    FERNANDO P G,RACHEL S,SEBASTIEN O. TorchIO:a Python library for efficient loading,preprocessing,augmentation and patch-based sampling of medical images in deep learning[J]. Computer Methods and Programs in Biomedicine,2021,208. DOI: 10.1016/J.CMPB.2021.106236.
    [23]
    赵杰,孙伟,徐中达,等. 基于形态学预处理的数字图像相关方法研究[J]. 实验力学,2022,37(5):629-637.

    ZHAO Jie,SUN Wei,XU Zhongda,et al. Study on the method of digital image correlation based morphological pre-processing[J]. Journal of Experimental Mechanics,2022,37(5):629-637.
    • 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(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (73) PDF downloads(7) 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