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Intelligent Image Analysis Technology and Application for Rail Track Inspection

  • Peng Dai
  • Shengchun WangEmail author
  • Zichen Gu
Chapter

Abstract

Based on the deep learning technology, the track image intelligent recognition algorithm has been developed, which has made significant progress in the analysis of rail track infrastructure inspection. The main work includes as follows: (1) research on intelligent recognition method of fastener defect based on convolutional neural network—by modeling the deep network from the fasteners image of the nationwide rail line, the detection rate of abnormal fasteners has been significantly improved and the false alarm rate has been greatly reduced. The research achievement has been applied in China’s railway inspection. (2) Research on foreign object recognition algorithm in ballastless track based on heuristic deep learning—aiming at the problem of foreign objects falling in the roadbed of the ballastless track, the system has a single type of foreign object recognition capability by labeling and iterative training of the limited samples. Then, by adding the new foreign objects found in the model to the training set for iterative training, a heuristic learning framework is constructed, which makes the system recognize a variety of foreign objects and has been put on probation in China’s high-speed rail to verify the outstanding performance of the recognition algorithm. The research work shows that the development of artificial intelligence technology and industry integration will collide with a fierce spark, and the continuous absorption of new technologies and new methods is the way to promote technological innovation in the industry.

Keywords

Deep learning Intelligent recognition Fastener defect Foreign object recognition 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Infrastructure Inspection Research Institute, China Academy of Railway Sciences Co., LtdBeijingChina
  2. 2.Beijing IMAP Technology Co., LtdBeijingChina

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