Volume 50 Issue 3
Mar.  2024
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XU Ciqiang, JIA Yunhong, TIAN Yuan. Large block coal detection algorithm for fully mechanized working face based on MES-YOLOv5s[J]. Journal of Mine Automation,2024,50(3):42-47, 141.  doi: 10.13272/j.issn.1671-251x.2024030009
Citation: XU Ciqiang, JIA Yunhong, TIAN Yuan. Large block coal detection algorithm for fully mechanized working face based on MES-YOLOv5s[J]. Journal of Mine Automation,2024,50(3):42-47, 141.  doi: 10.13272/j.issn.1671-251x.2024030009

Large block coal detection algorithm for fully mechanized working face based on MES-YOLOv5s

doi: 10.13272/j.issn.1671-251x.2024030009
  • Received Date: 2024-03-04
  • Rev Recd Date: 2024-03-22
  • Available Online: 2024-04-11
  • The objects in the fully mechanized working face have the features of high-speed motion, multi-scale, occlusion, etc. The existing object detection algorithms have problems such as low precision, large memory of models, and strong hardware dependence. In order to solve the above problems, a large block coal detection algorithm based on MES-YOLOv5s is proposed in fully mechanized working face. The method adopts a lightweight design, uses MobileNetV3 as the backbone network to reduce the memory occupied by the model and improve the detection speed on the CPU side. The method adds an efficient multi-scale attention (EMA) module to the neck network, fuses contextual information of different scales, and further reduces computational overhead. The method uses SIoU loss function instead of CIoU loss function to improve training speed and inference accuracy. The ablation experiment results show that MobileNetV3 significantly reduces the memory and detection time occupied by the model, but the mAP loss is severe. The EMA module and SIoU loss function can restore the precision of the loss to a certain extent, while ensuring that the model has a high detection speed on the CPU, meeting the real-time detection needs of coal mine underground objects. The comparative experimental results show that compared with DETR, YOLOv5n, YOLOv5s, and YOLOv7 models, the MES-YOLOv5s model has the best overall performance, with an mAP of 84.6%. The model occupies 11.2 MiB of memory and has a detection time of 31.8 ms on the CPU side. It can maintain high recall and precision in high-speed motion, multi-scale, occlusion, and multi-object working environments.

     

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  • [1]
    葛世荣,胡而已,李允旺. 煤矿机器人技术新进展及新方向[J]. 煤炭学报,2023,48(1):54-73.

    GE Shirong,HU Eryi,LI Yunwang. New progress and direction of robot technology in coal mine[J]. Journal of China Coal Society,2023,48(1):54-73.
    [2]
    谢和平,王金华,王国法,等. 煤炭革命新理念与煤炭科技发展构想[J]. 煤炭学报,2018,43(5):1187-1197.

    XIE Heping,WANG Jinhua,WANG Guofa,et al. New ideas of coal revolution and layout of coal science and technology development[J]. Journal of China Coal Society,2018,43(5):1187-1197.
    [3]
    王国法,庞义辉,任怀伟,等. 智慧矿山系统工程及关键技术研究与实践[J/OL]. 煤炭学报:1-23[2024-02-22]. https://doi.org/10.13225/j.cnki.jccs.2023.1355.

    WANG Guofa,PANG Yihui,REN Huaiwei,et al. System engineering and key technologies research and practice of smart mine[J/OL]. Journal of China Coal Society:1-23[2024-02-22]. https://doi.org/10.13225/j.cnki.jccs.2023.1355.
    [4]
    詹召伟. 煤矿综采工作面智能化开采关键技术和发展方向[J]. 能源与节能,2023(1):82-86.

    ZHAN Zhaowei. Key technologies and development direction of intelligent mining in fully mechanized mining face[J]. Energy and Energy Conservation,2023(1):82-86.
    [5]
    葛世荣,张晞,薛光辉,等. 我国煤矿煤机智能技术与装备发展研究[J]. 中国工程科学,2023,25(5):146-156. doi: 10.15302/J-SSCAE-2023.05.013

    GE Shirong,ZHANG Xi,XUE Guanghui,et al. Development of intelligent technologies and machinery for coal mining in China's underground coal mines[J]. Strategic Study of CAE,2023,25(5):146-156. doi: 10.15302/J-SSCAE-2023.05.013
    [6]
    崔卫秀,穆润青,解鸿章,等. 500 m超长工作面刮板智能输送技术研究[J/OL]. 煤炭科学技术:1-10[2024-02-22]. http://kns.cnki.net/kcms/detail/11.2402.TD.20230921.1538.002.html.

    CUI Weixiu,MU Runqing,XIE Hongzhang,et al. Research on intelligent conveying technology of 500 m ultra-long face scraper[J/OL]. Coal Science and Technology:1-10[2024-02-22]. http://kns.cnki.net/kcms/detail/11.2402.TD.20230921.1538.002.html.
    [7]
    苗长云,李佳. 基于机器视觉的带式输送机落料口堆煤检测[J]. 辽宁工程技术大学学报(自然科学版),2023,42(5):617-624.

    MIAO Changyun,LI Jia. Machine vision-based coal pile detection at drop port of belt conveyor[J]. Journal of Liaoning Technical University(Natural Science),2023,42(5):617-624.
    [8]
    程德强,寇旗旗,江鹤,等. 全矿井智能视频分析关键技术综述[J]. 工矿自动化,2023,49(11):1-21.

    CHENG Deqiang,KOU Qiqi,JIANG He,et al. Overview of key technologies for mine-wide intelligent video analysis[J]. Journal of Mine Automation,2023,49(11):1-21.
    [9]
    宋军强. OpenCV耦合机器视觉的背光板表面异物检测算法研究[J]. 组合机床与自动化加工技术,2015(11):83-87.

    SONG Junqiang. The study on image objects location and compensation technology based on OpenCV and machine vision[J]. Modular Machine Tool & Automatic Manufacturing Technique,2015(11):83-87.
    [10]
    刘孝军,王飞. 基于AI的煤矿视频智能分析技术[J]. 煤炭科学技术,2022,50(增刊2):260-264.

    LIU Xiaojun,WANG Fei. Application of video intelligent analysis technology in coal mine based on computer vision[J]. Coal Science and Technology,2022,50(S2):260-264.
    [11]
    曹现刚,李虎,王鹏,等. 基于跨模态注意力融合的煤炭异物检测方法[J]. 工矿自动化,2024,50(1):57-65.

    CAO Xiangang,LI Hu,WANG Peng,et al. A coal foreign object detection method based on cross modal attention fusion[J]. Journal of Mine Automation,2024,50(1):57-65.
    [12]
    高涵,赵培培,于正,等. 基于特征增强与Transformer的煤矿输送带异物检测[J/OL]. 煤炭科学技术:1-11[2024-02-22]. http://kns.cnki.net/kcms/detail/11.2402.td.20240119.1515.012.html.

    GAO Han,ZHAO Peipei,YU Zheng,et al. Coal mine conveyor belt foreign object detection based on feature enhancement and transformer[J/OL]. Coal Science and Technology:1-11[2024-02-22]. http://kns.cnki.net/kcms/detail/11.2402.td.20240119.1515.012.html.
    [13]
    张立亚. 基于生成对抗网络的带式输送机异物检测方法[J]. 工矿自动化,2023,49(11):53-59.

    ZHANG Liya. Foreign object detection method for belt conveyor based on generative adversarial nets[J]. Journal of Mine Automation,2023,49(11):53-59.
    [14]
    王科平,连凯海,杨艺,等. 基于改进YOLOv4的综采工作面目标检测[J]. 工矿自动化,2023,49(2):70-76.

    WANG Keping,LIAN Kaihai,YANG Yi,et al. Target detection of the fully mechanized working face based on improved YOLOv4[J]. Journal of Mine Automation,2023,49(2):70-76.
    [15]
    李江涛,张康辉,沙特. 煤中异物识别的深度学习模型轻量化策略[J]. 煤炭工程,2023,55(增刊1):220-224.

    LI Jiangtao,ZHANG Kanghui,SHA Te. Lightweight deep learning model compression strategy for coal foreign object recognition[J]. Coal Engineering,2023,55(S1):220-224.
    [16]
    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.

    GUI Fangjun,LI Yao. Coal gangue detection based on CBA-YOLO model[J]. Journal of Mine Automation,2022,48(6):128-133.
    [17]
    LIU Shu,QI Lu,QIN Haifang,et al. Path aggregation network for instance segmentation[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition,Salt Lake City,2018:8759-8768.
    [18]
    HOWARD A,SANDLER M,CHEN Bo,et al. Searching for MobileNetV3[C]. IEEE/CVF International Conference on Computer Vision,Seoul,2019:1314-1324.
    [19]
    OUYANG Daliang,HE Su,ZHANG Guozhong,et al. Efficient multi-scale attention module with cross-spatial learning[C]. IEEE International Conference on Acoustics,Speech and Signal Processing,Rhodes Island,2023:1-5.
    [20]
    YU Jiahui,JIANG Yuning,WANG Zhangyang,et al. UnitBox:an advanced object detection network[C]. The 24th ACM International Conference on Multimedia,2016:516-520. DOI: 10.1145/2964284.2967274.
    [21]
    GEVORGYAN Z. SIoU loss:more powerful learning for bounding box regression[EB/OL]. [2024-02-12]. https://arxiv.org/abs/2205.12740.
    [22]
    YANG Wenjuan,ZHANG Xuhui,MA Bing,et al. An open dataset for intelligent recognition and classification of abnormal condition in longwall mining[J]. Scientific Data,2023,10. DOI: 10.1038/s41597-023-02322-9.
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