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This paper proposes an integrated approach using acoustic analysis, computer vision, and real-time monitoring to enhance railway track safety and reliability. By analyzing data from a rolling camera mounted on a self-moving vehicle, the system detects track faults accurately. It also incorporates fire detection and engine detachment techniques for real-time monitoring. Additionally, it improves platform availability updates through advanced scheduling algorithms, optimizing train operations and passenger management. It has shown significant improvements in safety standards through experimentation, offering a comprehensive solution for track maintenance and safety management.

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Artificial Intelligence-Based Smart Automatic Railway Crack Detection and Protection

  • 章节

  • 发表日期：

    2026年

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  • Amrutha N. Daivagna,
  • Sujit Kumar,
  • Kavana Salimath,
  • Khaled Al-Qawasmi,
  • Deeksha Purushotham,
  • Devika S. Kumar,
  • Ishika Daga,
  • Jayant Giri

摘要

This paper proposes an integrated approach using acoustic analysis, computer vision, and real-time monitoring to enhance railway track safety and reliability. By analyzing data from a rolling camera mounted on a self-moving vehicle, the system detects track faults accurately. It also incorporates fire detection and engine detachment techniques for real-time monitoring. Additionally, it improves platform availability updates through advanced scheduling algorithms, optimizing train operations and passenger management. It has shown significant improvements in safety standards through experimentation, offering a comprehensive solution for track maintenance and safety management.