Volume 22 Issue 4
Aug.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(4): 186-195
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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