International Journal of Internet, Broadcasting and Communication

  • 제14권3호
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  • Pages.149-154
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  • 2022
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  • 2288-4920(pISSN)
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  • 2288-4939(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
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Current Trend and Direction of Deep Learning Method to Railroad Defect Detection and Inspection

  • Han, Seokmin (Major of Data Science, Korea National University of Transportation)
  • 투고 : 2022.06.19
  • 심사 : 2022.06.24
  • 발행 : 2022.08.31
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초록

In recent years, the application of deep learning method to computer vision has shown to achieve great performances. Thus, many research projects have also applied deep learning technology to railroad defect detection. In this paper, we have reviewed the researches that applied computer vision based deep learning method to railroad defect detection and inspection, and have discussed the current trend and the direction of those researches. Many research projects were targeted to operate automatically without visual inspection of human and to work in real-time. Therefore, methods to speed up the computation were also investigated. The reduction of the number of learning parameters was considered important to improve computation efficiency. In addition to computation speed issue, the problem of annotation was also discussed in some research projects. To alleviate the problem of time consuming annotation, some kinds of automatic segmentation of the railroad defect or self-supervised methods have been suggested.

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과제정보

This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2021R1F1A1049467) and by Korea National University of Transportation in 2022.

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