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Updated: Sep 13, 2025

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
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YOLO-Extreme: Obstacle Detection for Visually Impaired Navigation Under Foggy Weather.

Wei Wang1, Bin Jing1, Xiaoru Yu1

  • 1College of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, China.

Sensors (Basel, Switzerland)
|July 30, 2025
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Summary

We developed YOLO-Extreme, an advanced object detection system, to help visually impaired individuals navigate safely in fog. This AI-powered tool enhances obstacle detection in adverse weather, improving independent mobility.

Keywords:
Channel-Selective Fusion BlockDual-Branch Bottleneck BlockMulti-Dimensional Collaborative Attention ModuleRTTS foggy datasetYOLO-Extremefoggy environments

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Visually impaired individuals face navigation challenges, especially in fog.
  • Existing assistive technologies struggle with adverse weather conditions.

Purpose of the Study:

  • To develop a robust object detection framework for navigation assistance in foggy environments.
  • To enhance the safety and independence of visually impaired individuals.

Main Methods:

  • Proposed YOLO-Extreme, an enhanced YOLOv12 framework.
  • Introduced Dual-Branch Bottleneck Block (DBB), Multi-Dimensional Collaborative Attention Module (MCAM), and Channel-Selective Fusion Block (CSFB).
  • Conducted experiments on Real-world Task-driven Traffic Scene (RTTS) foggy and Foggy Cityscapes datasets.

Main Results:

  • YOLO-Extreme achieved state-of-the-art detection accuracy and high inference speed.
  • Outperformed existing dehazing-and-detect and mainstream object detection methods.
  • Demonstrated superior performance on both RTTS foggy and Foggy Cityscapes datasets.

Conclusions:

  • YOLO-Extreme significantly improves navigation reliability and safety for visually impaired individuals in fog.
  • The framework shows strong generalization capabilities across different foggy urban scenes.
  • Offers practical value for real-world deployment of assistive navigation systems.