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Automated visual inspection of target parts for train safety based on deep learning

Content Provider	IET Digital Library
Author	Zhou, Fuqiang Song, Ya Liu, Liu Zheng, Dongtian
Abstract	Visual inspection of target parts is a common approach to ensuring train safety. However, some key parts, such as fastening bolts, do not possess sufficient feature information, because they are usually small, polluted, or obscured. These factors affect inspection accuracy and can lead to serious accidents. Therefore, traditional visual inspection relying on feature extraction cannot always meet the requirements of high-accuracy inspection. Deep learning has considerable advantages in image recognition for autonomous information mining, but it requires a considerable amount of computation. To resolve the issues mentioned above, this study proposes a method that combines traditional visual inspection with deep learning. Traditional feature extraction is used to locate the targets approximately, which makes the deep learning purposeful and efficient. A composite neural network, stacked auto-encoder convolutional neural network (SAE-CNN), is provided to further improve the training efficiency. A SAE is added to a CNN so that the network can obtain optimum results faster and more accurately. Taking the inspection of centre plate bolts in a moving freight car as an example, the overall system and specific processes are described. The study results showed satisfactory accuracy. A related analysis and comparative experiment were also conducted.
Starting Page	550
Ending Page	555
Page Count	6
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 6, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2016.0338
Journal	IET Intelligent Transport Systems
Publisher Date	2018-02-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Visual Inspection Autonomous Information Mining Centre Plate Bolts Composite Neural Network Computer Vision And Image Processing Technique Data Handling Technique Data Mining Deep Learning Feature Extraction Image Recognition Inspection Inspection Accuracy Knowledge Engineering Technique Learning in AI Moving Freight Car Neural Computing Technique Neural Nets Railway Safety SAE-CNN Stacked Auto-encoder Convolutional Neural Network Target Parts Traffic Engineering Computing Train Safety Training Efficiency
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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