Abstract
Rail track surfaces could suffer from defects such as abrasion and deformation, thus to ensure rail system safety, the conditions of rail tracks must be monitored. With the advancement of deep learning and computer vision technologies, automatic detection and classification techniques are being tested to replace or complement manual patrolling for productivity improvement. However, classic neural network approaches require a large amount of data which could be time-consuming and limit the application of deep learning techniques. This paper proposes applying one-shot learning using a Siamese convolutional neural network to the identification of rail surface defects. The results show the reduced requirement of training speed and possess potentials for real-time applications.
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This research is part of the project supported by SMRT Corporation Ltd.
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Ji, A., Quek, Y.T., Wong, E., Woo, W.L. (2022). Identification of Rail Surface Defects Based on One-Shot Learning. In: Zhang, Z. (eds) 2021 6th International Conference on Intelligent Transportation Engineering (ICITE 2021). ICITE 2021. Lecture Notes in Electrical Engineering, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-19-2259-6_73
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DOI: https://doi.org/10.1007/978-981-19-2259-6_73
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