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DOI: 10.17586/1023-5086-2019-86-12-65-75
Abnormal railway fastener detection using minimal significant regions and local binary patterns
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Publication in Journal of Optical Technology
Hong Fan, Qiang Wang, Yun Luo, Bailin Li Abnormal railway fastener detection using minimal significant regions and local binary patterns (Обнаружение дефектов в клеммных креплениях рельсов с использованием минимальных значимых областей и локальных бинарных шаблонов) [на англ. яз.] // Оптический журнал. 2019. Т. 86. № 12. С. 65–75. http://doi.org/10.17586/1023-5086-2019-86-12-65-75
Hong Fan, Qiang Wang, Yun Luo, Bailin Li Abnormal railway fastener detection using minimal significant regions and local binary patterns (Обнаружение дефектов в клеммных креплениях рельсов с использованием минимальных значимых областей и локальных бинарных шаблонов) [in English] // Opticheskii Zhurnal. 2019. V. 86. № 12. P. 65–75. http://doi.org/10.17586/1023-5086-2019-86-12-65-75
Hong Fan, Qiang Wang, Yun Luo, and Bailin Li, "Abnormal railway fastener detection using minimal significant regions and local binary patterns," Journal of Optical Technology. 86(12), 799-807 (2019). https://doi.org/10.1364/JOT.86.000799
Railway fastener is an important part of the railway system. Keeping the fasteners effective is essential to ensure the safe operation of the railway, so abnormal railway fastener detection is a main task of railway maintenance. With the development of railway system, the traditional manual fastener detection method has been unable to meet the application requirements, because it is very slow, costly, and dangerous. In this paper, we propose a novel method for abnormal fastener detection based on computer vision, which can detect missing and ectopic fasteners automatically. In this method, Minimal Significant Region is extracted in order to improve the fastener localization accuracy. Then, fastener recognition is operated using local binary features and Support Vector Machine classifier based on the fastener sub-images which are obtained by fastener localization. The proposed method is evaluated in our own database which is obtained by railway inspection system in different environments. The experimental results have shown improved performance against the state-of-the-art algorithm.
railway fastener, image classification, visual inspection, machine vision
Acknowledgements:This work was supported by Sichuan Province Science and Technology Support Program Project under Grant 2018GZ0361. The authors also thank the anonymous reviewers for their constructive comments and suggestions.
OCIS codes: 150.1135
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