SCueU-Net: Efficient Damage Detection Method for Railway Rail

Automatic detection of industrial product damage using machine learning is a promising research area. At the same time, various machine learning methods based on convolutional neural networks have a very important role in the application of visual automatic detection. Therefore, the machine vision-b...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.125109-125120
Main Authors: Lu, Jun, Liang, Bo, Lei, Qujiang, Li, Xiuhao, Liu, Junhao, Liu, Ji, Xu, Jie, Wang, Weijun
Format: Article
Language:English
Subjects:
Artificial neural networks
Damage detection
data augmentation
Deep learning
Feature extraction
High speed rail
High-speed railway
Inspection
Machine learning
Machine vision
rail damage detection
Rail transportation
Rails
Segmentation
Visualization
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Summary:Automatic detection of industrial product damage using machine learning is a promising research area. At the same time, various machine learning methods based on convolutional neural networks have a very important role in the application of visual automatic detection. Therefore, the machine vision-based automatic detection of high-speed railway rail damage has received widespread attention. This paper proposes an efficient detection method for the damage of high-speed railway rails called SCueU-Net. For the first time, the combination of U-Net graph segmentation network and the saliency cues method of damage location is applied to the task of high-speed railway rail damage detection. The experimental results show that our method has a detection accuracy rate of 99.76%, which is 6.74% higher than the recent method in damage identification accuracy, which improves the detection efficiency of rail damage significantly.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3007603