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http://dx.doi.org/10.7782/JKSR.2017.20.3.374

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

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)
Publication Information
Journal of the Korean Society for Railway / v.20, no.3, 2017 , pp. 374-381 More about this Journal
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.
Keywords
Concrete sleeper; Automatic crack identification; Pattern recognition; Adaboost; High resolution image;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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