Volume 22 Issue 4
Aug.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(4): 186-195
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LI Qing-yong, WANG Jian-zhu, ZHU Ye-zhou, HUANG Qi-long, PENG Wen-juan, WANG Sheng-chun, DAI Peng. Abnormal fastener detection model based on deep convolutional autoencoder with structural similarity[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 186-195. doi: 10.19818/j.cnki.1671-1637.2022.04.014
Citation: LI Qing-yong, WANG Jian-zhu, ZHU Ye-zhou, HUANG Qi-long, PENG Wen-juan, WANG Sheng-chun, DAI Peng. Abnormal fastener detection model based on deep convolutional autoencoder with structural similarity[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 186-195. doi: 10.19818/j.cnki.1671-1637.2022.04.014
shu

Abnormal fastener detection model based on deep convolutional autoencoder with structural similarity

doi: 10.19818/j.cnki.1671-1637.2022.04.014
  • 1.

    Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China

  • 2.

    Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing 100044, China

  • 3.

    Infrastructure Inspection Research Institute, China Academy of Railway Sciences Co., Ltd., Beijing 100081, China

Funds:

National Natural Science Foundation of China U2034211

Fundamental Research Funds for the Central Universities 2022JBQY007

More Information
  • Author Bio:

    LI Qing-yong(1979-), male, professor, PhD, liqy@bjtu.edu.cn

  • Received Date: 2021-03-02
    Available Online: 2022-10-08
  • Publish Date: 2022-08-25
    Abstract
  • The basic functions of rail fastener system were presented, the existing detection technologies for abnormal rail fasteners were outlined, and the concerns and shortcomings of machine vision-based traditional methods and deep learning methods were summarized. The basic idea and formalization process of autoencoder were explained, and an abnormal fastener detection model based on an encoding-decoding architecture was proposed. The drawbacks of traditional pixel-level image similarity metrics were analyzed, and the loss function and image abnormality were determined according to the structural similarity. A dataset of rail fastener images was built and utilized to verify the performance of the proposed model. The representative false-positive and false-negative images were visualized, and their appearance features were described, and the possible reasons for the occurrence of false positives and false negatives were analyzed. According to the research results, the detection performance of the proposed model is significantly enhanced by the structural similarity index. The F-value of the proposed model is 14.5% and 16.2%, respectively, higher than those of the models that have the same network architecture but use the mean absolute error and mean square error as the similarity metrics. The highest detection precision and F-value, as high as 98.6% and 98.1%, respectively, are achieved by the proposed model when it is compared with the other models under comparison. They are found to be 6% and 9.8%, respectively, higher than those of the second-best RotNet model. The recall of the proposed model is 97.1%, slightly lower than the 98.4% of the deep support vector data description (DSVDD) model. On the whole, an F-value over 9% higher than those of all the other models under comparison is achieved by the proposed model, representing a significant performance advantage of the proposed model.

     

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    • References(33)
  • [1]
    ZHAN Zhi-kun, SUN Hao, YU Xiao-dong, et al. Wireless rail fastener looseness detection based on MEMS accelerometer and vibration entropy[J]. IEEE Sensors Journal, 2020, 20(6): 3226-3234. doi: 10.1109/JSEN.2019.2955378
    [2]
    YUAN Zhan-dong, ZHU Sheng-yang, YUAN Xuan-cheng, et al. Vibration-based damage detection of rail fastener clip using convolutional neural network: experiment and simulation[J]. Engineering Failure Analysis, 2021, 119: 104906. doi: 10.1016/j.engfailanal.2020.104906
    [3]
    CHANDRAN P, RANTATALO M, ODELIUS J, et al. Train-based differential eddy current sensor system for rail fastener detection[J]. Measurement Science and Technology, 2019, 30(12): 125105. doi: 10.1088/1361-6501/ab2b24
    [4]
    WEI Xiu-kun, YANG Zi-ming, LIU Yu-xin, et al. Railway track fastener defect detection based on image processing and deep learning techniques: a comparative study[J]. Engineering Applications of Artificial Intelligence, 2019, 80: 66-81. doi: 10.1016/j.engappai.2019.01.008
    [5]
    JING Guo-qing, QIN Xuan-yang, WANG Hao-yu, et al. Developments, challenges, and perspectives of railway inspection robots[J]. Automation in Construction, 2022, 138: 104242. doi: 10.1016/j.autcon.2022.104242
    [6]
    ZHANG Hui, SONG Ya-nan, WANG Yao-nan, et al. Review of rail defect non-destructive testing and evaluation[J]. Chinese Journal of Scientific Instrument, 2019, 40(2): 11-25. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201902002.htm
    [7]
    WANG Ping, SHENG Hong-wei, JI Kai-lun, et al. Application and development trend of non-destructive testing technology for high-speed transportation facilities[J]. Journal of Data Acquisition and Processing, 2020, 35(2): 195-209. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SJCJ202002002.htm
    [8]
    LIU Jia-jia, XIONG Ying, LI Bai-lin, et al. Research on automatic inspection algorithm for railway fastener defects based on computer vision[J]. Journal of the China Railway Society, 2016, 38(8): 73-80. (in Chinese) doi: 10.3969/j.issn.1001-8360.2016.08.011
    [9]
    FAN Hong, COSMAN P C, HOU Yun, et al. High-speed railway fastener detection based on a line local binary pattern[J]. IEEE Signal Processing Letters, 2018, 25(6): 788-792. doi: 10.1109/LSP.2018.2825947
    [10]
    GIBERT X, PATEL V M, CHELLAPPA R. Robust fastener detection for autonomous visual railway track inspection[C]// IEEE. 2015 IEEE Winter Conference on Applications of Computer Vision. New York: IEEE, 2015: 694-701.
    [11]
    DAI Xian-xing, DING Shi-hai, YANG En-hui, et al. Development and verification of automatic inspection system for high-speed railway fastener[J]. Journal of Railway Science and Engineering, 2018, 15(10): 2478-2486. (in Chinese) doi: 10.19713/j.cnki.43-1423/u.2018.10.004
    [12]
    FENG Hao, JIANG Zhi-guo, XIE Feng-ying, et al. Automatic f astener classification and defect detection in vision-based railway inspection systems[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(4): 877-888. doi: 10.1109/TIM.2013.2283741
    [13]
    DAI Peng, WANG Sheng-chun, DU Xin-yu, et al. Image recognition method for the fastener defect of ballastless track based on semi-supervised deep learning[J]. China Railway Science, 2018, 39(4): 43-49. (in Chinese) doi: 10.3969/j.issn.1001-4632.2018.04.07
    [14]
    DONG Bang-yi, LI Qing-yong, WANG Jian-zhu, et al. An end-to-end abnormal fastener detection method based on data synthesis[C]//IEEE. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence. New York: IEEE, 2019: 149-156.
    [15]
    MA Ning-ning, ZHANG Xiang-yu, ZHENG Hai-tao, et al. ShuffleNet v2: Practical guidelines for efficient CNN architecture design[C]//Springer. 15th European Conference on Computer Vision. Berlin: Springer, 2018: 116-131.
    [16]
    LIU Jun-bo, HUANG Ya-ping, ZOU Qi, et al. Learning visual similarity for inspecting defective railway fasteners[J]. IEEE Sensors Journal, 2019, 19(16): 6844-6857. doi: 10.1109/JSEN.2019.2911015
    [17]
    WEI Ruo-yu, LI Shu-ting, WU Song-rong, et al. Defect detection of track fastener based on improved YOLO V3 algorithm[J]. Railway Standard Design, 2020, 64(12): 30-36. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDBS202012008.htm
    [18]
    REDMON J, FARHADI A. YOLO v3: an incremental improvement[J]. arXiv, 2018, https://doi.org/10.48550/arXiv.1804.02767
    [19]
    HSIEH C C, LIN Y W, TSAI L H, et al. Offline deep-learning-based defective track fastener detection and inspection system[J]. Sensors and Materials, 2020, 32(10): 3429-3442. doi: 10.18494/SAM.2020.2921
    [20]
    ZHAN You, DAI Xian-xing, YANG En-hui, et al. Convolutional neural network for detecting railway fastener defects using a developed 3D laser system[J]. International Journal of Rail Transportation, 2021, 9(5): 424-444. doi: 10.1080/23248378.2020.1825128
    [21]
    CUI Hao, LI Jian, HU Qing-wu, et al. Real-time inspection system for ballast railway fasteners based on point cloud deep learning[J]. IEEE Access, 2020, 8: 61604-61614. doi: 10.1109/ACCESS.2019.2961686
    [22]
    RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536.
    [23]
    MAAS A L, HANNUN A Y, NG A Y. Rectifier nonlinearities improve neural network acoustic models[C]//ICML. 2013 International Conference on Machine Learning Workshop on Deep Learning for Audio, Speech and Language Processing. New York: ICML, 2013: 1-6.
    [24]
    KALMAN B L, KWASNY S C. Why tanh: choosing a sigmoidal function[C]//IEEE. 1992 International Joint Conference on Neural Networks. New York: IEEE, 1992: 578-581.
    [25]
    WANG Zhou, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
    [26]
    BERGMANN P, LOWE S, FAUSER M, et al. Improving unsupervised defect segmentation by applying structural similarity to autoencoders[C]//Springer. 2019 International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Berlin: Springer, 2019: 372-380.
    [27]
    KINGMA D P, BA J L. Adam: a method for stochastic optimization[C]//ICLR. 2015 International Conference on Learning Representations. La Jolla: ICLR, 2015: 1-15.
    [28]
    IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]// ICML. 2015 International Conference on Machine Learning. New York: ICML, 2015: 448-456.
    [29]
    COVER T, HART P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27.
    [30]
    RUFF L, VANDERMEULEN R, GOERNITZ N, et al. Deep one-class classification[C]//ICML. 2018 International Conference on Machine Learning. New York: ICML, 2018: 4393-4402.
    [31]
    KOMODAKIS N, GIDARIS S. Unsupervised representation learning by predicting image rotations[C]//ICLR. 2018 International Conference on Learning Representations. La Jolla: ICLR, 2018: 1-16.
    [32]
    HE Kai-ming, ZHANG Xiang-yu, REN Shao-qing, et al. Deep residual learning for image recognition[C]//IEEE. 2016 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 770-778.
    [33]
    PUKELSHEIM F. The three sigma rule[J]. The American Statistician, 1994, 48(2): 88-91.
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