Deep Architecture for High-Speed Railway Insulator Surface Defect Detection: Denoising Autoencoder With Multitask Learning

The insulator is an important catenary component that maintains the insulation between the catenary and earth. Due to the long-term impact of railway vehicles and the environment, defects in the insulator are inevitable. Recently, automatic catenary inspection using computer vision and pattern recog...

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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 68; číslo 8; s. 2679 - 2690
Hlavní autori:	Kang, Gaoqiang, Gao, Shibin, Yu, Long, Zhang, Dongkai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Automation Catenary Computer vision deep denoising autoencoder (DDAE) defect detection Defects Feature extraction High speed rail high-speed railway Image classification Image segmentation Impact strength Inspection Insulation insulator Insulators Monte Carlo methods multitask learning (MTL) Neural networks Noise reduction Pattern recognition Rail transportation Surface defects
ISSN:	0018-9456, 1557-9662
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