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TFP-YOLO: Obstacle and Traffic Sign Detection for Assisting Visually Impaired Pedestrians.

Zhiwei Zheng1, Jin Cheng1, Fanghua Jin1

  • 1School of Science, Beijing Information Science and Technology University, Beijing 100192, China.

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|September 27, 2025
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Summary

This study introduces TFP-YOLO, an improved computer vision algorithm for machine guide dogs. It enhances the detection of traffic signs and obstacles, boosting safety for visually impaired individuals.

Keywords:
YOLOv8computer visionmachine guide dogobject detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Visually impaired individuals require advanced mobility assistance.
  • Machine guide dogs offer a scalable alternative to traditional guide dogs.
  • Existing systems need improved perception in complex urban environments.

Purpose of the Study:

  • To enhance the visual perception of machine guide dogs.
  • To improve the detection of traffic signs and small obstacles.
  • To increase the safety and reliability of intelligent mobility assistance.

Main Methods:

  • Developed TFP-YOLO, an improved YOLOv8-based detection algorithm.
  • Incorporated Triplet Attention and Triple Feature Encoding (TFE) modules.
  • Utilized a P2 detection head and WIoU loss function for enhanced accuracy and stability.

Main Results:

  • Achieved 93.9% detection accuracy and 90.2% precision.
  • Reduced model parameters by 17.2%.
  • Significantly improved detection of traffic lights, crosswalks, pedestrians, and bicycles.

Conclusions:

  • TFP-YOLO enhances machine guide dog perception in complex scenarios.
  • The algorithm provides robust technical support for path planning and deployment.
  • Demonstrates significant practical value for visually impaired mobility assistance.