Vision AI

Overview

Vision AI is a project designed to assist blind and visually impaired individuals by providing easy navigation and situational awareness through advanced deep learning algorithms. This system uses object detection, scene description notifications, immediate alert sensing, and range finding to identify potential threats and anomalies. Additionally, it fosters community building by connecting users with a support network.

Features

Object Detection: Recognizes and identifies objects in the user's environment.
Scene Description Notification: Provides auditory descriptions of the surroundings.
Immediate Alert Sensing: Detects and alerts users to immediate dangers or unusual situations.
Range Finder: Measures distance to obstacles and identifies threats and anomalies in the environment.
Community Building: Connects users with a community for support and information sharing.

Technologies Used

Azure Vision: Utilized for its robust computer vision capabilities.
TensorFlow, Keras, PyTorch: Used for model development and training.
Custom Neural Networks: Developed to handle specific tasks and improve system performance.

Installation

To set up the Vision AI project, follow these steps:

Clone the repository:

git clone https://github.com/yourusername/vision-ai.git
cd vision-ai

Create and activate a virtual environment:

python3 -m venv env
source env/bin/activate

Install the required dependencies:
```
pip install -r requirements.txt
```

Usage

Object Detection:

python object_detection.py --input path_to_image_or_video --output path_to_save_results

Scene Description Notification:

python scene_description.py --input path_to_image_or_video --output path_to_save_audio_description

Immediate Alert Sensing:

python alert_sensing.py --input path_to_real_time_feed --output path_to_save_alerts

Range Finder:

python range_finder.py --input path_to_real_time_feed --output path_to_save_results

Community Building:
- Connect with the community through the provided interface in the application.

Model Development

Object Detection

Models Used: Faster R-CNN, YOLOv3
Frameworks: TensorFlow, Keras
Training: Trained on a diverse dataset of common objects with data augmentation to improve robustness.

Scene Description

Models Used: Image Captioning models, Attention Mechanisms
Frameworks: TensorFlow, PyTorch
Training: Trained on a large dataset with paired images and descriptive text.

Immediate Alert Sensing

Models Used: Custom neural networks designed for real-time threat detection.
Frameworks: PyTorch
Training: Trained on video feeds with annotated alerts for various dangerous scenarios.

Range Finder

Models Used: Depth estimation models
Frameworks: TensorFlow
Training: Trained on stereo image datasets to accurately estimate distances.

Community Building

The community feature connects users with each other, allowing them to share experiences, provide support, and receive updates about new features and improvements.

Contributions

Contributions are welcome! Please fork the repository and submit a pull request with your improvements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

Thanks to the contributors and the community for their support and feedback.
Special thanks to Microsoft Azure for providing the Vision API.
This project utilizes open-source libraries such as TensorFlow, Keras, and PyTorch.

Contact

For any questions or inquiries, please contact [your email].

Feel free to adjust the sections to better match your project's specifics and details.

HARSHANKUMARHRK / Vision-AI