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AI Object Detection Web App

Description

This project is a React-based web application that performs real-time object detection using TensorFlow.js and the COCO-SSD (Common Objects in Context - Single Shot Detector) model. It also includes a custom training feature that allows users to add and detect their own object classes.

Features

  • Real-time object detection using device camera
  • Pre-trained detection for 80+ object classes using COCO-SSD
  • Custom object class training and detection
  • Live mode and Detect-and-Save mode
  • FPS (Frames Per Second) counter in live mode
  • Object count statistics
  • Detailed view of detected objects

Prerequisites

Before you begin, ensure you have met the following requirements:

  • Node.js (v12.0 or later)
  • npm (usually comes with Node.js)

Installation

  1. Clone the repository:

    git clone https://github.com/your-username/ai-detection-app.git
cd ai-detection-app

  2. Install the dependencies:

    npm install

Usage

  1. Start the development server:

    npm start

  2. Open your web browser and navigate to http://localhost:3000

  3. Grant camera permissions when prompted

  4. The app will start in live detection mode. You can switch between live mode and detect-and-save mode using the button provided.

  5. To add a custom class:

    • Enter a name for your new class in the input field
    • Click "Add Class"
    • Upload one or more images for this class using the file input that appears

  6. The app will now attempt to detect your custom objects alongside the pre-trained COCO-SSD objects.

How It Works

  • The app uses TensorFlow.js to load and run the COCO-SSD model in the browser.
  • For custom object detection, it uses a simple image similarity comparison.
  • In live mode, the app continuously captures frames from the camera and runs object detection on each frame.
  • In detect-and-save mode, you can capture a single frame for analysis.

Limitations

  • Custom object detection is based on whole-image similarity, not localized object detection.
  • Training data (uploaded images) is stored in memory and will be lost when the page is refreshed.
  • Performance may vary depending on the device's capabilities.

Future Improvements

  • Implement proper storage for training data (e.g., IndexedDB or server-side storage)
  • Add more advanced custom training capabilities
  • Improve custom object detection accuracy
  • Add ability to export and import trained custom models

Contributing

Contributions to this project are welcome. Please fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • TensorFlow.js team for providing the tools and models
  • React team for the excellent web application framework

Contact

If you have any questions or feedback, please open an issue in the GitHub repository.

Happy detecting!