Volume 48 Issue 9
Sep.  2022
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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
shu

Segmentation method of the abnormal area of coal infrared thermal image

  • 1.

    The National Joint Engineering Laboratory of Internet Applied Technology of Mines, Xuzhou 221008, China

  • 2.

    School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China

  • 3.

    School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221008, China

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  • Received Date: March 27, 2022
  • Revised Date: September 05, 2022
  • Available Online: June 20, 2022
    Graphical Abstract
    • Abstract
  • Infrared radiation can reflect the damage of coal and rock under load, and can be used to monitor and prevent the dynamic disaster of coal and rock. But the infrared thermal image generated by the infrared thermal imager has low pixel resolution and large noise, which leads to the detection result being greatly affected by subjective factors. Therefore, the damaged area of the coal body cannot be accurately identified. It has become a trend to combine deep learning with infrared thermal imaging for nondestructive testing. But the research on the identification and detection of coal damage under load by combining deep learning and infrared thermal imaging is relatively few. In order to solve the above problems, a segmentation method of the abnormal area of coal infrared thermal image based on multi-scale channel attention module (MS-CAM) U-Net model is proposed. The MS-CAM is introduced into the encoder of the traditional U-Net model, and the U-Net model structure based on MS-CAM is designed. The model not only pays attention to the major characteristics of the coal infrared thermal image abnormal area, but also pays attention to the small target characteristics of the abnormal area, so as to improve the segmentation accuracy of the abnormal area. In order to reduce the influence of the lack of coal infrared thermal image data set on the accuracy and applicability of the model, the data enhancement operation is carried out on the created coal infrared thermal image data set. The MS-CAM-based U-Net model is pre-trained by using the MS COCO data set. Then the coal infrared thermal image data set is used for training to obtain the final network weight. The experimental result shows that the method can effectively segment the abnormal areas of the infrared thermal image of the coal body. The accuracy rate, the F1 score, the Dice coefficient and the average cross-combination ratio are 94.75%, 94.94%, 94.65%, and 90. 03% respectively. The results are superior to the Deeplab model, the U-Net model and the U-Net model based on the attention mechanism of the SENet.
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    • References (20)
  • [1]
    程富起,李忠辉,魏洋,等. 基于单轴压缩红外辐射的煤岩损伤演化特征[J]. 工矿自动化,2018,44(5):64-70. DOI: 10.13272/j.issn.1671-251x.2017110064

    CHENG Fuqi,LI Zhonghui,WEI Yang,et al. Coal-rock damage evolution characteristics based on infrared radiation under uniaxial compression[J]. Industry and Mine Automation,2018,44(5):64-70. DOI: 10.13272/j.issn.1671-251x.2017110064
    [2]
    娄全,李忠辉,李爱国,等. 混凝土变形破坏的红外辐射特征研究[J]. 工矿自动化,2015,41(7):44-48.

    LOU Quan,LI Zhonghui,LI Aiguo,et al. Research of infrared radiation characteristics of concrete deformation and failure[J]. Industry and Mine Automation,2015,41(7):44-48.
    [3]
    TIAN He,LI Zhonghui,SHEN Xiaofan,et al. Identification method of infrared radiation precursor information of coal sample failure and instability under uniaxial compression[J]. Infrared Physics & Technology,2021,119:103957.
    [4]
    SUN Hai,MA Liqiang,LIU Wei,et al. The response mechanism of acoustic and thermal effect when stress causes rock damage[J]. Applied Acoustics,2021,180:108093. DOI: 10.1016/j.apacoust.2021.108093
    [5]
    LIU Wei,MA Liqiang,SUN Hai,et al. Using the characteristics of infrared radiation b-value during the rock fracture process to offer a precursor for serious failure[J]. Infrared Physics & Technology,2021,114:103644.
    [6]
    宋晶晶,李忠辉,张昕,等. 岩样损伤红外热像的归一化直方图表征研究[J]. 红外技术,2021,43(8):777-783.

    SONG Jingjing,LI Zhonghui,ZHANG Xin,et al. Research on normalized histogram characterization of infrared thermal image of rock sample damage[J]. Infrared Technology,2021,43(8):777-783.
    [7]
    CAO K,YUAN Q,XIE G,et al. Infrared radiation characteristics during crack development in water-bearing sandstone[J]. Soil Mechanics and Foundation Engineering,2021,58(3):209-214. DOI: 10.1007/s11204-021-09730-2
    [8]
    LI Zhonghui,YIN Shan,NIU Yue,et al. Experimental study on the infrared thermal imaging of a coal fracture under the coupled effects of stress and gas[J]. Journal of Natural Gas Science and Engineering,2018,55:444-451. DOI: 10.1016/j.jngse.2018.05.019
    [9]
    ZHANG Xueliang,WANG Deliang. Deep learning based binaural speech separation in reverberant environments[J]. IEEE/ACM Transactions on Audio,Speech,and Language Processing,2017,25(5):1075-1084. DOI: 10.1109/TASLP.2017.2687104
    [10]
    WANG Bin,DONG Ming,REN Ming,et al. Automatic fault diagnosis of infrared insulator images based on image instance segmentation and temperature analysis[J]. IEEE Transactions on Instrumentation and Measurement,2020,69(8):5345-5355. DOI: 10.1109/TIM.2020.2965635
    [11]
    AKRAM M W,LI Guiqiang,JIN Yi,et al. Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning[J]. Solar Energy,2020,198:175-186. DOI: 10.1016/j.solener.2020.01.055
    [12]
    BANG H T,PARK S,JEON H. Defect identification in composite materials via thermography and deep learning techniques[J]. Composite Structures,2020,246:112405. DOI: 10.1016/j.compstruct.2020.112405
    [13]
    DAI Yimian, GIESEKE F, OEHMCKE S, et al. Attentional feature fusion[C]. The IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, 2021: 3560-3569.
    [14]
    IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]. International Conference on Machine Learning, Lille, 2015: 448-456.
    [15]
    SHARMA G, LIU D, MAJI S, et al. Parsenet: a parametric surface fitting network for 3D point clouds[C]. European Conference on Computer Vision, Glasgow, 2020: 261-276.
    [16]
    WU Yuxin,HE Kaiming. Group normalization[J]. International Journal of Computer Vision,2018,128(3):742-755.
    [17]
    TAN Chuanqi, SUN Fuchuan, KONG Tao, et al. A survey on deep transfer learning[C]. International Conference on Artificial Neural Networks, Rhodes, 2018: 270-279.
    [18]
    LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C]. European Conference on Computer Vision, Zurich, 2014: 740-755.
    [19]
    MILLETARI F, NAVAB N, AHMADI S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C]. The 4th International Conference on 3D Vision, Stanford, 2016: 565-571.
    [20]
    SHELHAMER E,LONG J,DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,39(4):640-651.
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