15 November 2017 On-line bolt-loosening detection method of key components of running trains using binocular vision
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060513 (2017) https://doi.org/10.1117/12.2286728
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
Bolt loosening, as one of hidden faults, affects the running quality of trains and even causes serious safety accidents. However, the developed fault detection approaches based on two-dimensional images cannot detect bolt-loosening due to lack of depth information. Therefore, we propose a novel online bolt-loosening detection method using binocular vision. Firstly, the target detection model based on convolutional neural network (CNN) is used to locate the target regions. And then, stereo matching and three-dimensional reconstruction are performed to detect bolt-loosening faults. The experimental results show that the looseness of multiple bolts can be characterized by the method simultaneously. The measurement repeatability and precision are less than 0.03mm, 0.09mm respectively, and its relative error is controlled within 1.09%.
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Yanxia Xie, Yanxia Xie, Junhua Sun, Junhua Sun, } "On-line bolt-loosening detection method of key components of running trains using binocular vision", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060513 (15 November 2017); doi: 10.1117/12.2286728; https://doi.org/10.1117/12.2286728
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