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🎯 Object Detection System

Welcome to the repository for the Object Detection System! 🚀
This project demonstrates the power of computer vision and deep learning to detect and classify objects in real time. By leveraging state-of-the-art object detection algorithms, it provides accurate and efficient solutions for various applications, from security systems to autonomous vehicles. 🛡️🚗

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🧠 Overview

The Object Detection System identifies and localizes objects within images and video streams.
It’s built on top of powerful machine learning frameworks, ensuring high accuracy and fast inference times.

✨ Key Highlights:

• Multi-object detection with bounding boxes and class labels.
• Real-time processing with live video feeds.
• Scalable and customizable for diverse use cases.

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💻 Tech Stack

• Frameworks & Libraries: TensorFlow, Keras, OpenCV
• Programming Language: Python
• Algorithms Used: YOLO, SSD, Faster R-CNN
• Deployment Tools: Flask, TensorFlow Lite, Streamlit (for visualization)

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🚀 Features

• Real-Time Detection: Analyze live video streams and detect multiple objects simultaneously.
• High Accuracy Models: Leverages pre-trained models like YOLOv4 and SSD.
• Customizability: Fine-tune for specific object classes and datasets.
• User-Friendly Interface: Streamlit dashboard for easy interaction and visualization.

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📂 Project Structure

Object-Detection-System/
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├── models/               # Pre-trained models and weights
├── scripts/              # Core Python scripts for detection
├── data/                 # Sample datasets
├── app.py                # Flask/Streamlit application
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

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🛠️ Installation

Prerequisites

1. Python (3.7 or later)
2. Virtual Environment (optional but recommended)
3. Required Libraries: TensorFlow, Keras, OpenCV

Steps

1. Clone the repository:

   git clone https://github.com/durjaysamrat/Object-Detection-System.git  

2. Navigate to the project folder:

   cd Object-Detection-System  

3. Install dependencies:

   pip install -r requirements.txt  

4. Run the application:

   streamlit run app.py  

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🖼️ Example Outputs

🔍 Input:

Image of a busy street

🧠 Output:

• Objects Detected: Car, Pedestrian, Traffic Light
• Bounding Boxes drawn with class labels

📸 Sample Detection:

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🌟 Use Cases

• Security Systems: Real-time monitoring for intrusions.
• Retail Analytics: Analyze customer behavior and store traffic.
• Autonomous Vehicles: Detect pedestrians, vehicles, and traffic signals.
• Healthcare: Medical imaging and diagnostics.

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🤝 Contributions

Contributions are welcome! 🎉
Feel free to fork this repository, work on exciting features, and submit a pull request.

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📫 Connect

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⭐ Star this repository if you find it helpful!
Let’s explore the future of object detection together. 🎯

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