🧠 Smart Object Detection (YOLOv8)

Smart Object Detection is a modern, real-time object detection system powered by YOLOv8, OpenCV, and PyTorch. It detects multiple objects in both images and videos, displaying and saving annotated results automatically in dedicated folders.

🚀 Features

• 🖼️ Detect objects in images and videos
• ⚡ Real-time FPS tracking for video detection
• 💾 Automatically saves processed outputs in organized folders
• 🔍 Powered by Ultralytics YOLOv8 — the latest in real-time computer vision
• 🧰 Easy to customize and extend

📂 Folder Structure

smart-object-detection/
│
├── Input Frame/        # Place your input videos here
├── Input Images/       # Place your input images here
├── Output Frame/       # Processed videos with detection boxes
├── Output images/      # Processed images with bounding boxes
│
├── image_detection_yolov8.py   # Script for image detection
├── video_detection_yolov8.py   # Script for video detection
├── yolov8n.pt                  # YOLOv8 Nano weights
├── Requirements.txt            # Dependencies list
├── README.md                   # Documentation
└── .gitignore                  # Ignored files (e.g. venv, runs, media)

⚙️ Installation

1️⃣ Clone the Repository

git clone https://github.com/mazharsnow/smart-object-detection.git
cd smart-object-detection

2️⃣ Create a Virtual Environment

python -m venv venv
.\venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r Requirements.txt

🧪 Usage

🔹 Image Detection

1. Place your image in the Input Images folder.
2. Run the script:

python image_detection_yolov8.py

3. The processed image will be saved in the Output images folder.

🔹 Video Detection

1. Place your video in the Input Frame folder.
2. Run the script:

python video_detection_yolov8.py

3. The processed video will be saved in the Output Frame folder. FPS and detection results will be displayed in real time.

📦 Requirements

• Python >= 3.8
• opencv>=4.10.0.84
• numpy>=1.26.3
• pandas>=2.2.3
• torch>=2.3.0
• ultralytics>=8.3.0
• matplotlib>=3.9.2
• scikit-learn>=1.5.2
• albumentations>=1.4.8

Install everything using:

pip install -r Requirements.txt

🧠 Technologies Used

Library	Purpose
YOLOv8 (Ultralytics)	Object detection model
OpenCV	Image and video processing
PyTorch	Deep learning framework
NumPy / Pandas	Data handling
Matplotlib	Visualization (optional)

📸 Example Results

Input	Output

Input	Output

🧰 Future Enhancements

• Add object counting and tracking
• Integrate with webcam/live stream
• Add distance estimation and object measurement
• Deploy as a web app with Streamlit

👨‍💻 Author

Mazharul Islam Tusar
📧 mazharul.tusar@outlook.com
💻 GitHub: @mazharsnow

🏷️ GitHub Topics

object-detection, yolov8, computer-vision, deep-learning, pytorch, opencv,
ultralytics, real-time-detection, ai-project, python

📜 License

This project is released under the MIT License — free to use, modify, and distribute with attribution.