Open AccessAutomated visual inspection of target parts for train safety based on deep learning
Author(s) -
Zhou Fuqiang,
Song Ya,
Liu Liu,
Zheng Dongtian
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2016.0338
Subject(s) - convolutional neural network , artificial intelligence , deep learning , visual inspection , computer science , feature (linguistics) , feature extraction , key (lock) , encoder , artificial neural network , visualization , computer vision , engineering , pattern recognition (psychology) , philosophy , linguistics , computer security , operating system
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.