Volume 50 Issue 5
May  2024
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(5): 52-59
TENG Wenxiang, WANG Cheng, FEI Shuhui. Research on coal gangue recognition algorithm based on HGTC-YOLOv8n model[J]. Journal of Mine Automation，2024，50（5）：52-59.  doi: 10.13272/j.issn.1671-251x.2024030064
Citation: TENG Wenxiang, WANG Cheng, FEI Shuhui. Research on coal gangue recognition algorithm based on HGTC-YOLOv8n model[J]. Journal of Mine Automation，2024，50（5）：52-59.  doi: 10.13272/j.issn.1671-251x.2024030064
shu

Research on coal gangue recognition algorithm based on HGTC-YOLOv8n model

doi: 10.13272/j.issn.1671-251x.2024030064
  • 1.

    School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China

  • 2.

    Collaborative Innovation Center of Mine Intelligent Equipment and Technology, Anhui University of Science and Technology, Huainan 232001, China

  • 3.

    Anhui Zhongke Photoelectric Color Sorter Machinery Co., Ltd., Postdoctoral Research Station, Hefei 231200, China

  • Received Date: 2024-03-26
  • Rev Recd Date: 2024-05-24
    • Available Online: 2024-06-13
    Abstract
  • The existing deep learning based coal gangue recognition methods have problems in complex working conditions such as low lighting, high noise, and motion blur in coal mines, such as low precision of coal gangue recognition, easy omission of small target coal gangue, large model parameter and computational complexity, and difficulty in deploying to devices with limited computing resources. A coal gangue recognition algorithm based on the HGTC-YOLOv8n model is proposed. The method replaces the backbone network of YOLOv8n with HGNetv2 network, effectively extracts multi-scale features to improve coal gangue recognition performance and reduces model storage requirements and computational resource consumption. The method embeds a Triplet Attention mechanism module in the backbone network to capture interaction information between different dimensions. The method enhances the extraction of target features in coal gangue images, and reduces the interference of irrelevant information. The method selects the content aware reassembly of features(CARAFE) to improve the upsampling operator of YOLOv8n neck feature fusion network, utilizing contextual information to enhance perceptual field of view and improve the accuracy of small target coal gangue recognition. The experimental results show the following points.① The average precision of the HGTC-YOLOv8n model is 93.5%, the parameters number of the model is 2.645×106, the number of floating-point operation is 8.0×109, and the frame rate is 79.36 frames/s. ② The average precision of the YOLOv8n model has increased by 2.5% compared to the YOLOv8n model, and the number of parameters and floating-point operations have decreased by 16.22% and 10.11%, respectively. ③ The comparison results with the YOLO series models show that the HGTC-YOLOv8n model has the highest average precision, the least number of parameters and floating-point operations, fast detection speed, and the best overall detection performance. ④ The coal gangue recognition algorithm based on the HGTC-YOLOv8n model has improved the low precision of coal gangue recognition and the easy omission of small target coal gangue under complex working conditions in coal mines. The method meets the requirements of real-time detection of coal gangue images.

     

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    • References(24)
  • [1]
    谢和平,任世华,谢亚辰,等. 碳中和目标下煤炭行业发展机遇[J]. 煤炭学报,2021,46(7):2197-2211.

    XIE Heping,REN Shihua,XIE Yachen,et al. Development opportunities of the coal industry towards the goal of carbon neutrality[J]. Journal of China Coal Society,2021,46(7):2197-2211.
    [2]
    王国法,杜毅博,任怀伟,等. 智能化煤矿顶层设计研究与实践[J]. 煤炭学报,2020,45(6):1909-1924.

    WANG Guofa,DU Yibo,REN Huaiwei,et al. Top level design and practice of smart coal mines[J]. Journal of China Coal Society,2020,45(6):1909-1924.
    [3]
    冯来宏,李克相,顾雷雨,等. 我国井下智能干选技术装备发展及展望[J]. 煤炭工程,2023,55(9):11-15.

    FENG Laihong,LI Kexiang,GU Leiyu,et al. Development and prospect of underground intelligent dry separation technology and equipment in China[J]. Coal Engineering,2023,55(9):11-15.
    [4]
    张创业,王晓川,刘庆军,等. 近全岩保护层开采煤矸井下分选及充填一体化技术研究与应用[J]. 煤炭工程,2023,55(10):6-11.

    ZHANG Chuangye,WANG Xiaochuan,LIU Qingjun,et al. Integrated technology of coal gangue underground separation and filling in full rock protection seam mining[J]. Coal Engineering,2023,55(10):6-11.
    [5]
    WANG Yuanbin,WANG Yujing,DANG Langfei. Video detection of foreign objects on the surface of belt conveyor underground coal mine based on improved SSD[J]. Journal of Ambient Intelligence and Humanized Computing,2020,14:5507-5516.
    [6]
    王家臣,李良晖,杨胜利. 不同照度下煤矸图像灰度及纹理特征提取的实验研究[J]. 煤炭学报,2018,43(11):3051-3061.

    WANG Jiachen,LI Lianghui,YANG Shengli. Experimental study on gray and texture features extraction of coal and gangue image under different illuminance[J]. Journal of China Coal Society,2018,43(11):3051-3061.
    [7]
    张红,李晨阳. 基于光学图像的煤矸石识别方法综述[J]. 煤炭工程,2022,54(7):159-163.

    ZHANG Hong,LI Chenyang. Review on coal gangue identification methods based on optical images[J]. Coal Engineering,2022,54(7):159-163.
    [8]
    郜亚松,张步勤,郎利影. 基于深度学习的煤矸石识别技术与实现[J]. 煤炭科学技术,2021,49(12):202-208. doi: 10.3969/j.issn.0253-2336.2021.12.mtkxjs202112025

    GAO Yasong,ZHANG Buqin,LANG Liying. Coal and gangue recognition technology and implementation based on deep learning[J]. Coal Science and Technology,2021,49(12):202-208. doi: 10.3969/j.issn.0253-2336.2021.12.mtkxjs202112025
    [9]
    李娟莉,魏代良,李博,等. 基于深度学习轻量化的改进SSD煤矸快速分选模型[J]. 东北大学学报(自然科学版),2023,44(10):1474-1480.

    LI Juanli,WEI Dailiang,LI Bo,et al. Improved SSD rapid separation model of coal gangue based on deep learning and light-weighting[J]. Journal of Northeastern University(Natural Science),2023,44(10):1474-1480.
    [10]
    李博,王学文,庞尚钟,等. 煤与矸石图像特征分析及试验研究[J]. 煤炭科学技术,2022,50(8):236-246.

    LI Bo,WANG Xuewen,PANG Shangzhong,et al. Image characteristics analysis and experimental study of coal and gangue[J]. Coal Science and Technology,2022,50(8):236-246.
    [11]
    郭永存,王希,何磊,等. 基于TW−RN优化CNN的煤矸识别方法研究[J]. 煤炭科学技术,2022,50(1):228-236. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201023

    GUO Yongcun,WANG Xi,HE Lei,et al. Research on coal and gangue recognition method based on TW-RN optimized CNN[J]. Coal Science and Technology,2022,50(1):228-236. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201023
    [12]
    徐志强,吕子奇,王卫东,等. 煤矸智能分选的机器视觉识别方法与优化[J]. 煤炭学报,2020,45(6):2207-2216.

    XU Zhiqiang,LYU Ziqi,WANG Weidong,et al. Machine vision recognition method and optimization for intelligent separation of coal and gangue[J]. Journal of China Coal Society,2020,45(6):2207-2216.
    [13]
    徐慈强,贾运红,田原. 基于MES−YOLOv5s的综采工作面大块煤检测算法[J]. 工矿自动化,2024,50(3):42-47,141.

    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.
    [14]
    张磊,王浩盛,雷伟强,等. 基于YOLOv5s−SDE的带式输送机煤矸目标检测[J]. 工矿自动化,2023,49(4):106-112.

    ZHANG Lei,WANG Haosheng,LEI Weiqiang. Coal gangue target detection of belt conveyor based on YOLOv5s-SDE[J]. Journal of Mine Automation,2023,49(4):106-112.
    [15]
    邓天民,程鑫鑫,刘金凤,等. 基于特征复用机制的航拍图像小目标检测算法[J]. 浙江大学学报(工学版),2024,58(3):437-448.

    DENG Tianmin,CHENG Xinxin,LIU Jinfeng,et al. Small target detection algorithm for aerial images based on feature reuse mechanism[J]. Journal of Zhejiang University(Engineering Science),2024,58(3):437-448.
    [16]
    WANG Gang,CHEN Yanfei,AN Pei,et al. UAV-YOLOv8:a small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios[J]. Sensors,2023,23(16). DOI: 10.3390/S23167190.
    [17]
    ZHANG Lei,ZHENG Jiachun,LI Chaopeng,et al. CCDN-DETR:a detection transformer based on constrained contrast denoising for multi-class synthetic aperture radar object detection[J]. Sensors,2024,24(6). DOI: 10.3390/S24061793.
    [18]
    VORUGUNTI C S,PULABAIGARI V,GORTHI R K S S,et al. Osvfusenet:online signature verification by feature fusion and depth-wise separable convolution based deep learning[J]. Neurocomputing,2020,409:157-172. doi: 10.1016/j.neucom.2020.05.072
    [19]
    何凯,程刚,王希,等. 基于CED−YOLOv5s模型的煤矸识别方法研究[J]. 工矿自动化,2024,50(2):49-56,82.

    HE Kai,CHENG Gang,WANG Xi,et al. Research on coal gangue recognition method based on CED-YOLOv5s model[J]. Journal of Mine Automation,2024,50(2):49-56,82.
    [20]
    SHIMAA S,KHALID A,PAWEL P,et al. Graph convolutional network with triplet attention learning for person re-identification[J]. Information Sciences,2022,617:331-345. doi: 10.1016/j.ins.2022.10.105
    [21]
    王瑞婷,王海燕,陈晓,等. 基于混合卷积与三重注意力的高光谱图像分类网络[J]. 智能系统学报,2023,18(2):260-269.

    WANG Ruiting,WANG Haiyan,CHEN Xiao,et al. Hyperspectral image classification based on hybrid convolutional neural network with triplet attention[J]. CAAI Transactions on Intelligent Systems,2023,18(2):260-269.
    [22]
    WANG Jiaqi,CHEN Kai,LIU Ziwei,et al. Carafe++:unified content-aware reassembly of features[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,44(9):4674-4687.
    [23]
    单慧琳,王硕洋,童俊毅,等. 增强小目标特征的多尺度光学遥感图像目标检测[J]. 光学学报,2024,44(6):382-394.

    SHAN Huilin,WANG Shuoyang,TONG Junyi,et al. Multi-scale optical remote sensing image target detection based on enhanced small target features[J]. Acta Optica Sinica,2024,44(6):382-394.
    [24]
    QIU Yongsheng,LU Yuanyao,WANG Yuantao,et al. IDOD-YOLOV7:image-dehazing YOLOV7 for object detection in low-light foggy traffic environments[J]. Sensors,2023,23(3). DOI: 10.3390/S23031347.
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