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Specifics of Data Collection and Data Processing during Formation of RailVista Dataset for Machine Learning- and Deep

Gulsipat Abisheva1, Nikolaj Goranin2, Bibigul Razakhova1

  • 1Department of Artificial Intelligence Technology, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana KZ-010000, Kazakhstan.

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|August 29, 2024
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Summary
This summary is machine-generated.

A new dataset, Rail Vista, was created for railway track defect detection using machine learning. This high-quality dataset aids in developing automated systems for enhanced railway safety and maintenance.

Keywords:
data collectiondatasetmachine learningrailwayrailway track defects

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Railway Engineering

Background:

  • Automated defect detection is crucial for railway safety and maintenance.
  • High-quality datasets are essential for training effective machine learning models.

Purpose of the Study:

  • To introduce the Rail Vista dataset for railway track defect detection.
  • To facilitate the development of advanced machine and deep learning models for railway infrastructure analysis.

Main Methods:

  • Collected 200,000 high-resolution images of railway tracks.
  • Categorized images into 19 distinct defect classes.
  • Employed advanced image capture and distortion techniques for data enrichment.
  • Stored data in a secure data warehouse using binary file formats.

Main Results:

  • Developed the comprehensive Rail Vista dataset.
  • The dataset supports training for diverse railway defect detection models.
  • Demonstrated the dataset's utility in advancing automated railway inspection.

Conclusions:

  • High-quality datasets are pivotal for machine learning in the railway sector.
  • The Rail Vista dataset significantly contributes to automated defect recognition.
  • Future work can leverage this dataset to improve railway safety and operational reliability.