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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Traffic Light Recognition Assistant for Color Vision Deficiency Using YOLO with Multilingual Audio Feedback.

Yinyuan Ma1, Fathan Arifah1, Qonita Afifah2

  • 1School of Mechatronic Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

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|February 27, 2026
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Summary
This summary is machine-generated.

This study developed a traffic light recognition system for drivers with color vision deficiency (CVD). The system uses spatial positioning and provides multilingual audio feedback, enhancing driving safety with intelligent transportation systems (ITSs).

Keywords:
assistive sensing technologycolor vision deficiencyintelligent transportation systemsspatial-position inferencetraffic light recognition

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Area of Science:

  • Intelligent Transportation Systems (ITSs)
  • Computer Vision
  • Assistive Technologies

Background:

  • Drivers with color vision deficiency (CVD) face significant challenges in recognizing traffic light colors, increasing driving risks.
  • Existing traffic signal recognition systems often rely on color perception, posing limitations for individuals with CVD.
  • The need for inclusive Intelligent Transportation Systems (ITSs) that cater to diverse user needs, including those with visual impairments, is growing.

Purpose of the Study:

  • To develop an innovative traffic light recognition system for drivers with CVD.
  • To provide offline, multilingual audio feedback (Indonesian, Mandarin, English) for enhanced user experience.
  • To reduce reliance on color-based perception in traffic signal recognition for assistive ITS applications.

Main Methods:

  • Implementation of a spatial-position inference framework using a YOLOv12 model with full-frame traffic light annotation.
  • Traffic light state recognition based on the relative positions of active lights, minimizing dependence on color information.
  • System evaluation using a comprehensive dataset encompassing diverse driving conditions (day/night, various weather, traffic densities).

Main Results:

  • The system achieved an average detection confidence of approximately 0.73, with a maximum confidence of 0.95.
  • Demonstrated low processing latency of 0.214 seconds on a CPU-only configuration, indicating real-time feasibility.
  • Successfully recognized traffic light states using spatial positioning rather than solely color.

Conclusions:

  • The developed traffic light recognition system offers a viable assistive tool for drivers with CVD.
  • The system enhances driving safety by providing an intelligent aid within ITS frameworks.
  • This approach contributes to more inclusive and accessible transportation for individuals with color vision deficiency.