Abstract:
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.
