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Proceedings Paper

Potential fault region detection in TFDS images based on convolutional neural network
Author(s): Junhua Sun; Zhongwen Xiao
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Paper Abstract

In recent years, more than 300 sets of Trouble of Running Freight Train Detection System (TFDS) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically due to some difficulties such as such as the diversity and complexity of faults and some low quality images. To improve the performance of automatic fault recognition, it is of great importance to locate the potential fault areas. In this paper, we first introduce a convolutional neural network (CNN) model to TFDS and propose a potential fault region detection system (PFRDS) for simultaneously detecting four typical types of potential fault regions (PFRs). The experimental results show that this system has a higher performance of image detection to PFRs in TFDS. An average detection recall of 98.95% and precision of 100% are obtained, demonstrating the high detection ability and robustness against various poor imaging situations.

Paper Details

Date Published: 25 October 2016
PDF: 8 pages
Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 101571L (25 October 2016); doi: 10.1117/12.2246551
Show Author Affiliations
Junhua Sun, BeiHang Univ. (China)
Zhongwen Xiao, BeiHang Univ. (China)


Published in SPIE Proceedings Vol. 10157:
Infrared Technology and Applications, and Robot Sensing and Advanced Control

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