Open AccessAnomaly Detection of Pantograph Based on Salient Segmentation and Generative Adversarial Networks
Author(s) -
Ye Wang,
Wei Quan,
Xuemin Lu,
Yuchen Peng,
Ning Zhou,
Dong Zou,
Yueping Liu,
S.Y. Guo,
Danyang Zheng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012140
Subject(s) - pantograph , catenary , salient , computer science , artificial intelligence , similarity (geometry) , computer vision , segmentation , pattern recognition (psychology) , image (mathematics) , engineering , engineering drawing , structural engineering
The pantograph is an important component of railway pantograph-catenary system, which can provide electric current for electrified railway electric locomotive. Since the pantograph is in an open roof environment, the defects of the pantograph are inevitable in the long run. In order to ensure the safe operation of trains, in this paper, we propose a new method for anomaly detection of pantograph based on salient segmentation and generative adversarial networks. First, an object location model is trained by U-Net which perform excellent properties for a small number of samples to accurately extract salient area of the pantograph. Second, a generative adversarial model is constructed to generate the reconstructed salient images of pantograph by vector mapping. Finally, the structural similarity algorithm is used to evaluate the similarity between the input salient image and the reconstructed salient image, so as to extract the image difference and realize the anomaly detection of the pantograph. Experimental results validate the effectiveness and accuracy of our approach.