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基于HGTC−YOLOv8n模型的煤矸识别算法研究

滕文想 王成 费树辉

滕文想,王成,费树辉. 基于HGTC−YOLOv8n模型的煤矸识别算法研究[J]. 工矿自动化,2024,50(5):52-59.  doi: 10.13272/j.issn.1671-251x.2024030064
引用本文: 滕文想,王成,费树辉. 基于HGTC−YOLOv8n模型的煤矸识别算法研究[J]. 工矿自动化,2024,50(5):52-59.  doi: 10.13272/j.issn.1671-251x.2024030064
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

基于HGTC−YOLOv8n模型的煤矸识别算法研究

doi: 10.13272/j.issn.1671-251x.2024030064
基金项目: 机械工业联合会矿山采选装备智能化重点实验室开放基金项目(2022KLMIO4);安徽理工大学引进人才基金项目(13230411)。
详细信息
    作者简介:

    滕文想(1990—),男,江苏徐州人,讲师,博士,主要从事机械动力学、机电装备设计、物料辅运机器人以及数值求解方法的教学与研究工作,E-mail:wxtengcumt@163.com

  • 中图分类号: TD67

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

  • 摘要: 现有基于深度学习的煤矸识别方法在煤矿井下低照度、高噪声及运动模糊等复杂工况下存在煤矸识别精度低、小目标煤矸容易漏检、模型参数量和运算量大,难以部署到计算资源有限的设备中等问题,提出了一种基于HGTC−YOLOv8n模型的煤矸识别算法。采用HGNetv2网络替换YOLOv8n的主干网络,通过多尺度特征的有效提取,提高煤矸识别效果并减少模型的存储需求和计算资源消耗;在主干网络中嵌入三重注意力机制模块Triplet Attention,捕获不同维度间的交互信息,增强煤矸图像目标特征的提取,减少无关信息的干扰;选用内容感知特征重组模块(CARAFE)来改进YOLOv8n颈部特征融合网络上采样算子,利用上下文信息提高感受视野,提高小目标煤矸识别准确率。实验结果表明:① HGTC−YOLOv8n模型的平均精度均值为93.5%,模型的参数量为2.645×106,浮点运算量为8.0×109 ,帧速率为79.36帧/s。② 平均精度均值较YOLOv8n模型提升了2.5%,参数量和浮点运算量较YOLOv8n模型分别下降了16.22%和10.11%。③ 与YOLO系列模型相比,HGTC−YOLOv8n模型的平均精度均值最高,且参数量和浮点运算量最少,检测速度较快,综合检测性能最佳。④ 基于HGTC−YOLOv8n模型的煤矸识别算法在煤矿井下复杂工况下,改善了煤矸识别精度低、小目标煤矸容易漏检等问题,满足煤矸图像实时检测要求。

     

  • 图  1  HGTC−YOLOv8n 模型结构

    Figure  1.  HGTC-YOLOv8n model structure

    图  2  HGStem结构和HGBlock结构

    Figure  2.  HGStem structure and HGBlock structure

    图  3  Triplet Attention结构

    Figure  3.  Triplet Attention structure

    图  4  CARAFE框架

    Figure  4.  Framework of content aware reassembly of features (CARAFE)

    图  5  煤矸数据集

    Figure  5.  Coal gangue dataset

    图  6  不同模型的mAP曲线

    Figure  6.  Mean average precision curves of different models

    图  7  不同算法在4种工况下的检测结果

    Figure  7.  Detection results of different algorithms under four working conditions

    图  8  带式输送机上煤矸识别及计数可视化

    Figure  8.  Visualization of coal gangue recognition and count on belt conveyor

    表  1  训练过程中数据增强的超参数

    Table  1.   Hyperparameters of data enhancement during training

    超参数
    色调增强 0.015
    饱和度增强 0.7
    亮度增强 0.4
    随机缩放 0.5
    水平翻转 0.5
    水平平移 0.1
    Mosic数据增强 1.0
    下载: 导出CSV

    表  2  消融实验结果

    Table  2.   Ablation experiment results

    模型 HGNetv2 Triplet Attention CARAFE mAP/% 参数量/106 浮点运算量/109 帧速率/(帧·s−1
    YOLOv8n × × × 91.0 3.157 8.9 81.96
    优化模型1 × × 92.0 2.503 7.7 84.74
    优化模型2 × × 92.8 3.157 8.9 80.00
    优化模型3 × × 92.8 3.297 9.1 80.00
    优化模型4 × 92.7 2.504 7.7 82.64
    优化模型5 × 92.1 2.644 8.0 81.30
    优化模型6 × 93.1 3.298 9.1 77.51
    优化模型7 93.5 2.644 8.0 79.36
    下载: 导出CSV

    表  3  不同模型的煤矸识别结果

    Table  3.   Coal gangue recognition results of different models

    模型 参数量/106 浮点运算量/109 mAP/% 帧速率/(帧·s−1
    YOLOv5s 7.025 16.0 92.7 73.52
    YOLOv7−tiny 6.018 13.2 90.8 68.96
    YOLOv8n 3.157 8.9 91.0 81.96
    YOLOv8s 11.167 28.8 91.9 78.12
    HGTC−YOLOv8n 2.645 8.0 93.5 79.36
    下载: 导出CSV
  • [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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出版历程
  • 收稿日期:  2024-03-26
  • 修回日期:  2024-05-24
  • 网络出版日期:  2024-06-13

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