Abstract
High deployment costs, safety risks, and time delays restrict traditional track detection methods in high-speed railways. Therefore, approaches based on optical sensors have become the most remarkable strategy in terms of deployment cost and real-time performance. Owing to the large amount of data obtained by sensors, it has been proven that deep learning, as a powerful data-driven approach, can perform effectively in the field of track detection. However, it is difficult and expensive to obtain labeled data from railways during operation. In this study, we used a segment of a high-speed railway track as the experimental object and deployed a distributed optical fiber acoustic system (DAS). We propose a track detection method that innovatively leverages semi-supervised deep learning based on image recognition, with a particular pre-processing for the dataset and a greedy algorithm for the selection of hyper-parameters. The superiority of the method was verified in both experiments and actual applications.
Highlights
High-Speed Railway Track DetectionTrack defect detection: As a result of the high frequency and intensity of track operation in open air, track defects occur constantly
We propose a track detection system that innovatively leverages semi-supervised deep learning based on image recognition
To increase the sample information density, we use multi-point amplitude rather than single-point; To alleviate the impact of the lack of high-frequency components caused by an insufficient sampling rate, we use amplitude rather than frequency features to train the model; We convert the data into images and classify the samples through a CNN network to achieve better convergence speed and capacity; We use the deep network to adaptively extract the sample features rather than manually extract them; We use semi-supervised learning to efficiently leverage unlabeled data to further improve the performance of the model
Summary
Track defect detection: As a result of the high frequency and intensity of track operation in open air, track defects occur constantly. There are four typical defects in track detection: Sensing. Bulges can be divided into multiple states according. Since the Stefano Faralli and Ali Masoudi monolithic concrete structure of the track and the reinforcement are densely distributed, it Received: 18 December 2021 is difficult to identify the defects inside. In addition to the lack of Accepted: 4 January 2022 real-time performance, the track detection vehicle has a long operating period and high. Published: 6 January 2022 deployment cost, and manual detection is restricted by the high error rate [2,3]. Note: neutral approaches based on optical sensors have become the most remarkable methods in2terms with regard to jurisdictional claims in of deployment costs and real-time performance.
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