25 October 2016 Potential fault region detection in TFDS images based on convolutional neural network
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Proceedings Volume 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control; 101571L (2016) https://doi.org/10.1117/12.2246551
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
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.
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Junhua Sun, Junhua Sun, Zhongwen Xiao, Zhongwen Xiao, } "Potential fault region detection in TFDS images based on convolutional neural network ", Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 101571L (25 October 2016); doi: 10.1117/12.2246551; https://doi.org/10.1117/12.2246551
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