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Self-adaptive rail edge detection for trams based on mathematical morphology.

Shizhong He1, Longjiang Shen1, Zuobing Zhou1

  • 1CRRC ZhuZhou Locomotive Co., LTD, Zhuzhou, China.

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|November 14, 2024
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Summary

A new self-adaptive algorithm accurately detects tram rail edges, crucial for identifying track obstructions and preventing collisions. This computer vision method shows strong performance, even with image noise, enhancing tram safety systems.

Keywords:
CannySelf-adaptivemorphologyrail edge detectiontram

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Area of Science:

  • Computer Vision
  • Image Processing
  • Transportation Safety

Background:

  • Increasing tram lines lead to more traffic incidents, especially collisions.
  • Identifying foreign objects on tracks is vital for tram operational safety.
  • Accurate rail edge detection is key for track area recognition and threat detection.

Purpose of the Study:

  • To propose a self-adaptive rail-edge detection algorithm for enhanced tram safety.
  • To accurately extract rail edges using mathematical morphology and computer vision.
  • To evaluate the algorithm's performance against existing methods.

Main Methods:

  • Developed a self-adaptive rail-edge detection algorithm.
  • Utilized mathematical morphology and computer vision techniques.
  • Compared the proposed algorithm with Canny and two other published methods.

Main Results:

  • The proposed algorithm demonstrated strong robustness across different scenes and noise conditions.
  • Performance was evaluated using Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and computational time.
  • The algorithm effectively identified rail edges in level crossing scenarios.

Conclusions:

  • The self-adaptive rail-edge detection algorithm offers a reliable solution for improving tram safety.
  • The method's robustness to noise makes it suitable for real-world applications.
  • This algorithm can be integrated into early warning systems for trams to detect rail edges and potential hazards.