Skip to content

Balaji-3009/SightSync

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

16 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

SightSync

Description: SightSync is a groundbreaking assistive technology designed specifically for visually impaired individuals. Powered by a Raspberry Pi 4, SightSync offers real-time perception of the surrounding environment and delivers text-to-speech responses to provide audible descriptions. By leveraging cutting-edge technologies such as OpenCV and Large Language Models, SightSync aims to enhance independence and navigation capabilities for users with visual impairments.

Features:

Real-time Perception:

SightSync provides instantaneous perception of the user's surroundings, enabling quick and accurate awareness of the environment.

Text-to-Speech Responses:

The device converts visual information into audible descriptions, offering users real-time feedback and guidance.

Advanced Technologies:

SightSync utilizes sophisticated tools like OpenCV and Large Language Models to ensure accurate and comprehensive descriptions of the surroundings.

Enhanced Independence:

By offering audible descriptions of the environment, SightSync empowers visually impaired individuals to navigate independently and with confidence.

Hardware Components

  1. Raspberry Pi 4
  2. Neo-6M GPS Module
  3. MPU6050 - 6 axis IMU
  4. Logitech C270 Webcam

Getting Started:

To get started with SightSync, follow these steps:

Hardware Setup:

Ensure you have a Raspberry Pi 4 available and properly configured.

Software Installation:

Install the necessary software components, including OpenCV and any required language models.

Connectivity:

Connect SightSync to any additional hardware components, such as IoT sensors or drones, as needed.

Testing:

Test SightSync in various environments to ensure optimal performance and accuracy.

About

Smart Vision Device using AI

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 66.4%
  • Jupyter Notebook 12.8%
  • HTML 11.6%
  • CSS 7.0%
  • JavaScript 1.4%
  • Mako 0.8%