Real-Time Object Detection Project - README

Project Overview

The Real-Time Object Detection project aims to identify and classify objects in live video feeds or static images with high accuracy and speed. This project leverages advanced machine learning models and computer vision libraries to provide seamless object detection capabilities for various use cases such as surveillance, autonomous vehicles, and augmented reality applications.

Features

• Real-Time Detection: Processes video streams in real time to detect objects with minimal latency.
• High Accuracy: Utilizes state-of-the-art deep learning models such as YOLO (You Only Look Once), SSD (Single Shot Detector), or Faster R-CNN for precise object detection.
• Multiple Object Classes: Detects and classifies multiple objects in a single frame.
• Customizable Model: Option to train and integrate custom object detection models.
• User Interface: Easy-to-use interface for live video or image uploads.

Installation

Follow these steps to set up the Real-Time Object Detection project:

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)
• CUDA Toolkit (optional, for GPU acceleration)

Clone the Repository

git clone https://github.com/mathivanan001/ai-Object_detection.git
cd ai-Object_detection

Create and Activate a Virtual Environment

python -m venv env
source env/bin/activate  # On Windows: .\env\Scripts\activate

Install Required Packages

pip install -r requirements.txt

Additional Setup

• Download Pre-trained Weights: If using pre-trained models like YOLO, download the weights and place them in the weights/ directory.
• Configure Model Settings: Modify config.py to select the desired object detection model and parameters.

Running the Object Detection

To start real-time object detection, run:

python detect.py

Options

• Image Detection: To detect objects in a static image:

  python detect.py --image path/to/image.jpg

• Webcam Detection: For live video feed from the webcam:

  python detect.py --webcam

Custom Model Training

To train a custom object detection model:

1. Prepare Dataset: Organize your dataset with labeled images.
2. Configure Training Script: Use train.py and set the necessary parameters in config.py.
3. Run Training:

   python train.py

License

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

Contact

For questions or feedback, please contact [mathivanan2276@gmail.com].