High deployment costs, safety risks, and time delays restrict traditional track detection click here 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 replica beach walk candle 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.