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Development of Automatic Crack Identification Algorithm for a Concrete Sleeper Using Pattern Recognition

패턴인식을 이용한 콘크리트침목의 자동균열검출 알고리즘 개발

  • Kim, Minseu (Institute of Railroad Convergence Technology, Korea National University of Transportation) ;
  • Kim, Kyungho (A BEST Co. Ltd.) ;
  • Choi, Sanghyun (Department of Railroad Facility Engineering, Korea National University of Transportation)
  • Received : 2017.02.10
  • Accepted : 2017.05.25
  • Published : 2017.06.30

Abstract

Concrete sleepers, installed on majority of railroad track in this nation can, if not maintained properly, threaten the safety of running trains. In this paper, an algorithm for automatically identifying cracks in a sleeper image, taken by high-resolution camera, is developed based on Adaboost, known as the strongest adaptive algorithm and most actively utilized algorithm of current days. The developed algorithm is trained using crack characteristics drawn from the analysis results of crack and non-crack images of field-installed sleepers. The applicability of the developed algorithm is verified using 48 images utilized in the training process and 11 images not used in the process. The verification results show that cracks in all the sleeper images can be successfully identified with an identification rate greater than 90%, and that the developed automatic crack identification algorithm therefore has sufficient applicability.

Acknowledgement

Grant : 고해상도 카메라를 이용한 콘크리트 침목균열자동검출 시스템

Supported by : 중소기업청, 한국교통대학교

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