Detection and recognition of high-speed railway catenary locator based on Deep Learning

doi:10.3969/j.issn.0253-2778.2017.04.006

Abstract

Abstract: High-speed rail monitoring is conducted mainly by adopting image processing and computer vision technology to detect, identify and track catenary components in image sequences taken by the visible light high-definition camera. In the entire monitoring system, the detection and recognition of the locator constitutes the very basis. It is difficult to design the feature descriptor with the characteristics of versatility, robustness and high-accuracy by using traditional target detection algorithms. #br##br#The detection of the high-accuracy locators based on the Faster R-CNN framework has been realized. Meanwhile, the Hough transform is used to detect the skeleton outline of the locator, and the optimal fitting straight line of the locator is extracted by the filtering mechanism, which paves the way for the non-contact precision measurement of the slope of the locators.

Key words: locator, target detection, Deep Learning, convolutional neural networks, Hough transform

CLC Number:

TP391

CHEN Dongjie, ZHANG Wensheng, YANG Yang. Detection and recognition of high-speed railway catenary locator based on Deep Learning[J]. Journal of University of Science and Technology of China, 2017, 47(4): 320-327.

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