Research Center of Network Management, Beijing Jiaotong University, Beijing 100044, China.
Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China.
Sensors (Basel). 2022 Jan 6;22(2):413. doi: 10.3390/s22020413.
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.
传统的高速铁路轨道检测方法存在部署成本高、安全风险大、时间延迟等问题。因此，基于光学传感器的方法在部署成本和实时性能方面成为最显著的策略。由于传感器获得的数据量很大，已经证明深度学习作为一种强大的数据驱动方法，可以在轨道检测领域有效地发挥作用。然而，在运行过程中从铁路上获取标记数据既困难又昂贵。在本研究中，我们使用一段高速铁路轨道作为实验对象，并部署了分布式光纤声学系统（DAS）。我们提出了一种基于图像识别的半监督深度学习的轨道检测方法，对数据集进行了特殊的预处理，并使用贪心算法选择超参数。该方法在实验和实际应用中都得到了验证。
