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Railway OHE Measurement and Monitoring System for High-Speed Operations

We solved one of the most complex challenges in railway infrastructure, accurately measuring overhead equipment (OHE) during high-speed train operations using AI and computer vision. The system enables real-time, automated detection and analysis of pantographs, masts, and contact wires, helping railways maintain safety, precision, and efficiency at scale.

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Summary :

Our AI- and computer vision–driven system automates the measurement of railway OHE parameters, including wire height, stagger, and mast alignment, in real-time. It achieves ±10 mm precision using high-frame-rate cameras with depth sensors, even when trains are in motion at high speed. The solution transforms manual inspection into data-driven monitoring through GPS-tagged analytics, ensuring safety and predictive maintenance across extensive railway routes.

Problem

Problem

Objectives

  • Achieve Real-Time Accuracy

     Ensure ±10 mm precision for wire height, stagger, and mast alignment measurements at high speed.

  • Enable Intelligent Automation

    Replace manual inspection with AI and computer vision analysis.

  • Integrate GPS Tracking

    Map each mast location accurately for maintenance records.

  • Deliver Instant Alerts

    Notify maintenance teams of deviations in real time.

  • Automate Data Logging

    Generate compliance-ready reports without manual intervention.

Challenges

  • Maintaining detection stability despite vibration and motion blur.
  • Processing 2K-resolution video streams at 60 FPS with minimal latency.
  • Synchronizing depth sensor data and GPS coordinates in real time.
  • Adapting to different lighting, weather, and operational environments.
  • Building a scalable system capable of handling data for 10 lakh+ masts.

Solution

  • Designed an advanced AI and computer vision system that detects and measures OHE components,  including pantographs, masts, and contact points in real time.
  • Used high-frame-rate cameras with depth sensors to capture precise 3D measurements for wire height, stagger, and alignment during train movement.
  • Optimized AI models for real-time edge inference, ensuring low latency and reliable performance under high-speed vibration and motion.
  • Built a unified data pipeline combining visual, spatial, and GPS inputs to generate accurate, traceable measurement logs.
  • Developed a desktop interface for live visualization, deviation alerts, and automatic reporting across large-scale networks.
  • Enabled continuous, high-precision monitoring that supports predictive maintenance and strengthens safety compliance.

Technologies

Architecture

Ingest
High-frame-rate cameras capture video and depth data with GPS synchronization.
Process
AI models detect OHE components like pantographs and masts in real time.
Measure
Depth data is converted into physical measurements for height, stagger, and distances.
Sync
GPS coordinates are aligned with visual data for accurate geo-tagging
Deliver
Live visualization, alerts, and structured Excel/CSV reports are generated for maintenance teams.

Results & Impact

Achieved ±10 mm precision in OHE measurement during high-speed runs

Enabled accurate detection in motion with stable AI performance

GPS-tagged measurements ensured traceable, location-based data

Reduced manual inspection time by 70 % through automation

Improved safety and predictive maintenance for railway infrastructure

Automated reporting for masts, improving compliance and data accuracy

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