NDLI: Detection of Component Types and Track Damage for High-Speed Railway Using Region-Based Convolutional Neural Networks

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Detection of Component Types and Track Damage for High-Speed Railway Using Region-Based Convolutional Neural Networks

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Li, Shengyuan Li, Peigang Zhang, Yang Zhao, Xuefeng
Copyright Year	2018
Abstract	High-speed railway plays critical roles in public safety and the country’s economy. Visual detection of components and damages can reflect the health conditions of high-speed railway. Human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. Image-based detection methods abandon the weakness of human-based visual inspection. However, in practice, the complex real-world situations, such as lighting and shadow changes, can lead to challenges to the wide adaptability of image process techniques. To overcome these challenges, this paper provides a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based detection method of component types and track damage for high-speed railway. To realize the method, a database including 575 images labeled for three component types and one track damage type of high-speed railway is built. A Faster R-CNN architecture based on ZF-Net is modified, then trained and validated using the built database. The performance of the trained Faster R-CNN is evaluated using 50 new images which are not be used for training process. The results show that the proposed method can indeed detect the component types and track damage for high-speed railway.
Sponsorship	Aerospace Division
File Format	PDF
ISBN	9780791851951
DOI	10.1115/SMASIS2018-8223
Volume Number	Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies
Conference Proceedings	ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
Language	English
Publisher Date	2018-09-10
Publisher Place	San Antonio, Texas, USA
Access Restriction	Subscribed
Subject Keyword	Economics Databases Artificial neural networks High speed rail Safety Damage Optical quality control Shades and shadows
Content Type	Text
Resource Type	Article

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