Improvement on detection performance for the angle of steady arm based on triggered image acquisition and deep learning

Yan-guo Wang

doi:10.1117/12.2600927

30 June 2021 Improvement on detection performance for the angle of steady arm based on triggered image acquisition and deep learning

Yan-guo Wang

Author Affiliations +

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118780G (2021) https://doi.org/10.1117/12.2600927
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

The angle of steady arm is an important inspection parameter of catenary. The existing manual measurement methods were unable to meet the requirements on measurement efficiency or measurement accuracy, which greatly restrict the efficiency of defects inspection and maintenance of catenary on high-speed railways. An automatic visual measurement method with excellent measurement efficiency was developed for the angle of steady arm, which can be used on catenary inspection car. However, the inspection reliability is poor for images with complicated background, such as images at the posts with multiple cantilever and between two posts with no steady arms. In order to solve the problem on system reliability, an upgraded visual measurement method is proposed in the paper. The camera system is changed into trigger image acquisition mode with a post detection module integrated, and the image detection algorithm for steady arm is greatly improved using deep convolutional neural networks, exploiting the research progress on object detection for catenary component. The proposed system has been fully tested on detection reliability, measurement repeatability and measurement accuracy, which shows much better reliability and availability.

Citation Download Citation

Yan-guo Wang "Improvement on detection performance for the angle of steady arm based on triggered image acquisition and deep learning", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118780G (30 June 2021); https://doi.org/10.1117/12.2600927

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