Real-Time Object Detection System Using YOLOv8

This project implements a real-time object detection system using the YOLOv8 Nano model and OpenCV. The system is capable of identifying over 80 commonly encountered objects from a webcam feed, such as people, furniture, animals, electronic devices, and structural elements. It is designed to be lightweight, modular, and easily extensible for research, surveillance, or industrial applications.

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Project Objectives

• Perform real-time object detection through a computer's webcam
• Detect multiple object classes from the COCO dataset
• Provide a modular codebase with clear separation of detection and visualization logic
• Offer an adaptable foundation for future enhancements, including model fine-tuning and real-world deployment

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Project Structure

object-detection-ai/
├── models/
│   └── yolov8n.pt             # Pretrained YOLOv8 Nano model
├── utils/
│   └── draw_boxes.py          # Utility for drawing bounding boxes and labels
├── realtime_detection.py      # Main script for webcam-based detection
├── requirements.txt           # Python dependencies
└── README.md                  # Project documentation

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Requirements

• Python 3.7 or higher
• Operating System: Windows, macOS, or Linux (GUI required)
• A functioning webcam
• pip package manager

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Installation and Setup

1. Clone the Repository

git clone https://github.com/Ahmadjamil888/REALTIME-OBJ-DETECTION-SURVIELLENCE.git
cd object-detection-ai

2. Install Dependencies

pip install -r requirements.txt

3. Download the YOLOv8 Model

Download the pretrained YOLOv8 Nano model file from the official Ultralytics release:

Download yolov8n.pt

Place the file in the models/ directory as follows:

object-detection-ai/models/yolov8n.pt

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Running the System

Execute the detection script from the root of the project directory:

python realtime_detection.py

To exit the detection window, press the `q` key.

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Customization

This system uses the YOLOv8 model pretrained on the COCO dataset. It can be customized for other use cases by:

• Replacing the model with a fine-tuned YOLO variant
• Modifying the draw_boxes.py logic to support additional metadata
• Integrating audio alerts, frame saving, or motion-based triggers

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License

This project is intended for educational and research purposes only. Consult relevant licensing terms for the YOLO model and third-party libraries before deploying in commercial or defense applications.