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煤体红外热像异常区域分割方法

赵小虎 车亭雨 叶圣 田贺 张凯

赵小虎,车亭雨,叶圣,等. 煤体红外热像异常区域分割方法[J]. 工矿自动化,2022,48(9):92-99.  doi: 10.13272/j.issn.1671-251x.2022030086
引用本文: 赵小虎,车亭雨,叶圣,等. 煤体红外热像异常区域分割方法[J]. 工矿自动化,2022,48(9):92-99.  doi: 10.13272/j.issn.1671-251x.2022030086
ZHAO Xiaohu, CHE Tingyu, YE Sheng, et al. Segmentation method of the abnormal area of coal infrared thermal image[J]. Journal of Mine Automation,2022,48(9):92-99.  doi: 10.13272/j.issn.1671-251x.2022030086
Citation: ZHAO Xiaohu, CHE Tingyu, YE Sheng, et al. Segmentation method of the abnormal area of coal infrared thermal image[J]. Journal of Mine Automation,2022,48(9):92-99.  doi: 10.13272/j.issn.1671-251x.2022030086

煤体红外热像异常区域分割方法

doi: 10.13272/j.issn.1671-251x.2022030086
基金项目: 中央高校基本科研业务费专项资金资助项目(2020ZDPY0223)。
详细信息
    作者简介:

    赵小虎(1976—),男,江苏徐州人,教授,博士,主要研究方向为矿山物联网、计算机网络和智能计算,E-mail:xiaohuzhao@126.com

    通讯作者:

    张凯(1982—),男,江苏徐州人,博士研究生,研究方向为智慧矿山网络优化,E-mail:kaizhang@cumt.edu.cn

  • 中图分类号: TD315/67

Segmentation method of the abnormal area of coal infrared thermal image

  • 摘要: 红外辐射可反映煤岩受载破坏情况,用于监测和预防煤岩动力灾害,但红外热像仪生成的红外热像图像素分辨率低、噪声较大,导致检测结果受主观因素影响较大,无法准确识别煤体损伤区域。将深度学习和红外热像结合进行无损检测已成为趋势,但目前结合深度学习和红外热像对煤体受载破坏进行识别检测的研究相对较少。针对上述问题,提出一种基于多尺度通道注意力模块(MS−CAM)U−Net模型的煤体红外热像异常区域分割方法。在传统U−Net模型的编码器中引入MS−CAM,设计了基于MS−CAM的U−Net模型结构,使模型在关注煤体红外热像异常区域显著特征的同时,还关注异常区域小目标特征,以提高异常区域分割精度。为降低煤体红外热像数据集匮乏对模型准确率和适用性的影响,对创建的煤体红外热像数据集进行数据增强操作,并采用MS COCO数据集对基于MS−CAM的U−Net模型进行预训练,再采用煤体红外热像数据集训练,得出最终网络权重。实验结果表明,该方法可有效分割煤体红外热像异常区域,精确率、F1分数、Dice系数和平均交并比分别为94.75%,94.94%,94.65%,90.03%,均优于Deeplab模型、U−Net模型和基于SENet注意力机制的U−Net模型。

     

  • 图  1  基于MS−CAM的U−Net模型结构

    Figure  1.  U-Net model structure based on multi-scale channel attention module(MS-CAM)

    图  2  MS-CAM结构

    Figure  2.  MS-CAM structure

    图  3  实验系统

    Figure  3.  Experimental system

    图  4  煤样3种加载破坏时期红外热像图

    Figure  4.  Three infrared thermal images of coal samples during loading pressure period

    图  5  LabelMe工具中煤样红外热像异常区域标注

    Figure  5.  Abnormal area tagging in infrared thermal images of coal samples in LabelMe tool

    图  6  部分增强图像

    Figure  6.  Partial enhanced images

    图  7  损失函数曲线

    Figure  7.  Loss function curve

    图  8  实验流程

    Figure  8.  Experiment process

    图  9  不同模型对煤体红外热像异常区域的分割结果

    Figure  9.  Segmentation results of infrared thermal images of coal samples by different models

    图  10  U−Net(SENet)模型和U−Net(MS−CAM)模型分割结果的类激活热力图

    Figure  10.  Class activation heat map of segmentation results by U-Net(SENet) model and U-Net(MS-CAM)model

    表  1  基于MS−CAM的U−Net模型网络结构及对应特征图

    Table  1.   Network structures of U-Net model based on MS-CAM and corresponding characteristic images

    网络结构特征图尺寸卷积核参数
    Conv_1 256×256×64
    256×256×64
    3×3×64
    3×3×64
    MS−CAM_1 256×256×64
    DownSampling_1 128×128×64 2×2
    Conv_2 128×128×128
    128×128×128
    3×3×128
    3×3×128
    MS−CAM_2 128×128×128
    DownSampling_2 64×64×128 2×2
    Conv_3 64×64×256
    64×64×256
    3×3×256
    3×3×256
    MS−CAM_3 64×64×256
    DownSampling_3 32×32×256 2×2
    Conv_4 32×32×512
    32×32×512
    3×3×512
    3×3×512
    MS−CAM_4 32×32×512
    DownSampling_4 16×16×512 2×2
    Conv 16×16×512
    16×16×512
    3×3×512
    3×3×512
    UpSampling_1 32×32×512 2×2
    Concatenate_1 32×32×1024 UpSampling_1+ MS−CAM_4
    conv_1 32×32×256
    32×32×256
    3×3×256
    3×3×256
    UpSampling_2 64×64×256 2×2
    Concatenate_2 64×64×512 UpSampling_2+ MS−CAM_3
    conv_2 64×64×128
    64×64×128
    3×3×128
    3×3×128
    UpSampling_3 128×128×128 2×2
    Concatenate_3 128×128×256 UpSampling_3+ MS−CAM_2
    conv_3 128×128×64
    128×128×64
    3×3×64
    3×3×64
    UpSampling_4 256×256×64 2×2
    Concatenate_4 256×256×128 UpSampling_4+ MS−CAM_1
    conv_4 256×256×64
    256×256×64
    3×3×64
    3×3×64
    Conv1×1 256×256×1 1×1×1
    下载: 导出CSV

    表  2  不同模型分割结果评价指标对比

    Table  2.   Comparison of evaluation indexes for segmentation results of different models %

    模型精确率F1分数Dice系数MIoU
    Deeplab88.6590.3187.9283.78
    U−Net92.2891.6791.5884.85
    U−Net(SENet)93.4693.5692.8187.28
    U−Net(MS−CAM)94.7594.9494.6590.03
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-28
  • 修回日期:  2022-09-06
  • 网络出版日期:  2022-06-21

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