2020
DOI: 10.1109/access.2020.3007603
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SCueU-Net: Efficient Damage Detection Method for Railway Rail

Abstract: 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 visionbased 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 fir… Show more

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Cited by 39 publications
(24 citation statements)
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References 60 publications
(73 reference statements)
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“…The proposed system autonomously detected the bolts that secure the rail to the sleepers and monitored the rail condition at high speeds, up to 460 km/h. VDSs are widely used for rail surface defect detection [ 22 , 23 , 24 , 25 ]. There are studies where the detection of defects of the railway plugs and fasteners using VDSs is analysed [ 26 , 27 ].…”
Section: Related Workmentioning
confidence: 99%
“…The proposed system autonomously detected the bolts that secure the rail to the sleepers and monitored the rail condition at high speeds, up to 460 km/h. VDSs are widely used for rail surface defect detection [ 22 , 23 , 24 , 25 ]. There are studies where the detection of defects of the railway plugs and fasteners using VDSs is analysed [ 26 , 27 ].…”
Section: Related Workmentioning
confidence: 99%
“…The non-conforming and unexpected patterns are called outliers or anomalies. Anomaly detection has been extensively used in a wide variety of applications such as cyber-intrusion detection [31], fraud detection [32], medical anomaly detection [33,34], industrial damage detection [35], hyperspectral image analysis [36], sensor networks [37], image processing [38], to cite just a few. Outlier detection is very popular in industrial applications [39][40][41][42][43] since it is critical to the efficient and secure operation of industrial equipment, integrated sensors, and the overall production process.…”
Section: Related Workmentioning
confidence: 99%
“…The improved method can obtain good performance, but it has disadvantages such as a slow detection speed and large detection model. Lu et al [20] proposed to apply the combined U-Net graph segmentation network and damage location method for damage detection of high-speed railways. This method can obtain a high detection accuracy but has the limitations of slow detection speed and large model volume.…”
Section: Introductionmentioning
confidence: 99%