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Related Experiment Video

Updated: Jun 11, 2025

Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind
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Improved yolov5 algorithm combined with depth camera and embedded system for blind indoor visual assistance.

Kaikai Zhang1, Yanyan Wang1, Shengzhe Shi1

  • 1School of Computer Science and Technology, Huaibei Normal University, 235000, Huaibei, China.

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|October 3, 2024
PubMed
Summary

This study introduces an improved YOLOv5 object detection algorithm for visually impaired individuals, enhancing an indoor object-finding device. The new system offers better portability and reduced costs, aiding daily life.

Keywords:
Attention mechanismsBidirectional feature pyramid networkGhostNetMachine visualizationSystem of finding objectsYOLOv5

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Area of Science:

  • Computer Vision
  • Assistive Technology
  • Artificial Intelligence

Background:

  • Existing indoor object-finding aids for the visually impaired suffer from poor portability, high costs, and environmental susceptibility.
  • There is a need for improved, cost-effective, and portable solutions to aid visually impaired individuals in daily object identification.

Purpose of the Study:

  • To develop an improved YOLOv5 algorithm and integrate it into an indoor object-finding device for the visually impaired.
  • To address the limitations of current assistive technologies by enhancing portability, reducing hardware costs, and improving performance.

Main Methods:

  • An improved YOLOv5 algorithm was developed using GhostNet as the backbone, incorporating a coordinate attention mechanism, and a bidirectional feature pyramid network.
  • The algorithm was integrated with a RealSense D435i depth camera and a voice system on a Raspberry Pi 4 B device.
  • The system processes voice commands for object identification and uses RGB and depth images for detection and ranging.

Main Results:

  • The improved YOLOv5 model achieved a 42.4% reduction in model size and a 47.9% reduction in parameters compared to the original YOLOv5.
  • The recall rate increased by 1.2% while maintaining the same precision.
  • The integrated device successfully detected and ranged objects, providing voice feedback on distances to assist users.

Conclusions:

  • The enhanced YOLOv5 algorithm significantly optimizes model size and parameters, making it suitable for portable assistive devices.
  • The developed indoor object-finding device effectively assists visually impaired individuals by providing accurate object detection, ranging, and voice feedback.
  • This technology offers a promising, cost-effective, and portable solution for enhancing the independence and daily living of the visually impaired.