Detection and Tracking of Pantograph and Cable Contact Points with Spark Identification

Overview

This project implements a computer vision-based system to detect, track, and analyze pantographs in railway videos. Using a trained YOLOv8 model, the system identifies pantograph components, tracks their movement, detects sparks, and logs all relevant information for performance and safety analysis. The solution is optimized for real-world deployment in railway monitoring systems.

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Features

• YOLOv8 Object Detection: Detects pantograph, pantobar, and cables in each video frame.
• Movement Tracking: Calculates horizontal and vertical displacement of the pantograph between frames.
• Spark Detection: Identifies sparks in the pantobar region by analyzing high-intensity (bright) spots.
• Contact Point Analysis: Computes distances between pantobar and cables to check for proper alignment.
• CSV Logging: Records timestamped movement data, detected sparks, and contact distances.
• Annotated Video Output: Saves the processed video with bounding boxes, annotations, and overlays.

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Setup

Requirements

• Python 3.8+

• Required Libraries:

  pip install opencv-python-headless numpy ultralytics

YOLO Model

• Ensure you have a trained YOLOv8 model for pantograph detection.
• Update the model_path variable in the script with the path to your weights file.

File Paths

• Input Video: Set input_video_path to the location of your raw video.
• Output Video: Set output_video_path where the processed annotated video will be saved.
• CSV File: Set csv_output_path to define where the CSV log should be stored.

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Script Workflow

1. Frame Capture: Reads each frame from the input video.
2. Object Detection: Runs YOLOv8 inference to detect pantograph components.
3. Movement Analysis: Compares pantograph position across consecutive frames.
4. Spark Detection: Identifies bright pixel clusters within the pantobar region.
5. Contact Analysis: Measures distances between pantobar and overhead cables.
6. Logging: Writes structured data to a CSV file.
7. Video Rendering: Saves annotated video with bounding boxes, spark highlights, and metrics overlay.

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Usage

1. Update paths for model, input video, output video, and CSV inside the script.

2. Run the script:

   python pantograph_tracking.py

3. Outputs:

   • Processed video with annotations.
   • CSV log file containing movement, spark count, and distances.

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Notes

• Ensure YOLO is properly trained for pantograph, pantobar, and cable classes.
• Detection parameters may require tuning for different lighting or video conditions.
• Output files will be saved in paths configured in the script.

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