Abisheva Gulsipat, Goranin Nikolaj, Razakhova Bibigul, Aidynov Tolegen, Satybaldina Dina
Department of Artificial Intelligence Technology, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana KZ-010000, Kazakhstan.
Department of Information Systems, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, LT-08412 Vilnius, Lithuania.
Sensors (Basel). 2024 Aug 13;24(16):5239. doi: 10.3390/s24165239.
This paper presents the methodology and outcomes of creating the Rail Vista dataset, designed for detecting defects on railway tracks using machine and deep learning techniques. The dataset comprises 200,000 high-resolution images categorized into 19 distinct classes covering various railway infrastructure defects. The data collection involved a meticulous process including complex image capture methods, distortion techniques for data enrichment, and secure storage in a data warehouse using efficient binary file formats. This structured dataset facilitates effective training of machine/deep learning models, enhancing automated defect detection systems in railway safety and maintenance applications. The study underscores the critical role of high-quality datasets in advancing machine learning applications within the railway domain, highlighting future prospects for improving safety and reliability through automated recognition technologies.
本文介绍了创建Rail Vista数据集的方法和成果，该数据集旨在使用机器学习和深度学习技术检测铁路轨道上的缺陷。该数据集包含20万张高分辨率图像，分为19个不同的类别，涵盖各种铁路基础设施缺陷。数据收集涉及一个细致的过程，包括复杂的图像捕获方法、用于数据扩充的失真技术，以及使用高效二进制文件格式在数据仓库中进行安全存储。这个结构化数据集有助于对机器学习/深度学习模型进行有效训练，增强铁路安全和维护应用中的自动缺陷检测系统。该研究强调了高质量数据集在推动铁路领域机器学习应用方面的关键作用，突出了通过自动识别技术提高安全性和可靠性的未来前景。
