LIN Li, LIU Xin, ZHU Junzhen, FENG Fuzhou. Classification of Ultrasonic Infrared Thermal Images Using a Convolutional Neural Network[J]. Infrared Technology , 2021, 43(5): 496-501.
Citation: LIN Li, LIU Xin, ZHU Junzhen, FENG Fuzhou. Classification of Ultrasonic Infrared Thermal Images Using a Convolutional Neural Network[J]. Infrared Technology , 2021, 43(5): 496-501.

Classification of Ultrasonic Infrared Thermal Images Using a Convolutional Neural Network

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  • Received Date: June 28, 2020
  • Revised Date: October 23, 2020
  • In the application of ultrasonic infrared thermographic technology, it is usually necessary to extract features from infrared thermographic images based on artificial experience and then adopt a pattern recognition method to classify the cracks. The identification and positioning process of the cracks is complicated, and the recognition rate is low. Therefore, a method of crack detection and recognition in ultrasonic infrared thermal images based on convolutional neural network technology is proposed in this paper. Its feature is that the features can be directly learned from the ultrasonic infrared image to realize the classification of infrared thermal images containing cracks. Thesis through the research experiment of metal plate specimen of the crack in and do not contain infrared thermal images, the convolutional neural network model is established for whether the image contains crack classification, the results show that the parameter optimized convolution neural network model for ultrasonic infrared thermal images of crack classification accuracy rate reached 98.7%.
  • [1]
    曾平平, 李林升. 基于卷积神经网络的水果图像分类识别研究[J]. 机械设计与研究, 2019, 35(1): 23-26, 34. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYY201901010.htm

    ZENG Pingping, LI Linsheng. Classification and Recognition of Common Fruit Images Based on Convolutional Neural Network[J]. Machine Design & Research, 2019, 35(1): 23-26, 34. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYY201901010.htm
    [2]
    林明旺. 基于卷积神经网络的鱼类图像识别与分类[J]. 电子技术与软件工程, 2017(6): 82-83. https://www.cnki.com.cn/Article/CJFDTOTAL-DZRU201706065.htm

    LIN Mingwang. Fish image recognition and classification based on convolutional neural network[J]. Electronic Technology & Software Engineering, 2017(6): 82-83. https://www.cnki.com.cn/Article/CJFDTOTAL-DZRU201706065.htm
    [3]
    张安安, 黄晋英, 冀树伟, 等. 基于卷积神经网络图像分类的轴承故障模式识别[J]. 振动与冲击, 2020, 39(4): 165-171. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202004021.htm

    ZHANG An'an, HUANG Jinying, JI Shuwei, et al. Bearing fault pattern recognition based on image classification with CNN[J]. Journal of Vibration and Shock, 2020, 39(4): 165-171. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202004021.htm
    [4]
    李玉鑑, 张婷, 单传辉, 等. 深度学习卷积神经网络从入门到精通[M]. 北京: 机械工业出版社, 2018.

    LI Yujian, ZHANG Ting, SHAN Chuanhui, et al. Deep Learning Convolutional Neural Network From Entry to Mastery[M]. Beijing: China Machine Press, 2018.
    [5]
    李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016, 36(9): 2508-2515, 2565. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201609029.htm

    LI Yandong, HAO Zongbo, LEI Hang. Survey of convolutional neural network[J]. Journal of Computer Applications, 2016, 36(9): 2508-2515, 2565. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201609029.htm
    [6]
    冯辅周, 张超省, 宋爱斌, 等. 超声红外热像检测中疲劳裂纹的检出概率模型研究[J]. 红外与激光工程, 2016, 45(3): 60-65. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201603008.htm

    FENG Fuzhou, ZHANG Chaosheng, SONG Aibin, et al. Probability of detection model for fatigue crack in ultrasonic infrared imaging[J]. Infrared and Laser Engineering, 2016, 45(3): 60-65. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201603008.htm
    [7]
    冯辅周, 张超省, 闵庆旭, 等. 超声红外热像技术中金属平板裂纹的生热特性[J]. 红外与激光工程, 2015, 44(5): 1456-14461. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201505012.htm

    FENG Fuzhou, ZHANG Chaosheng, MIN Qingxu, et al. Heating characteristics of metal plate crack in sonic IR imaging[J]. Infrared and Laser Engineering, 2015, 44(5): 1456-14461. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201505012.htm
    [8]
    Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems, 2012: 1106-1114.
    [9]
    Szegedy C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2015: 1-8.
    [10]
    HE K, ZHANG X, REN S, et al. Deep Residual Learning for Image Recognition[EB/OL]. [2020-6-20]. https://arxiv.org/pdf/1512.03385.pdf.
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