
Object Detection System with OpenCV and Python
Object Detection System with OpenCV and Python
📁 Project Overview
This professional-level object detection system enables accurate detection of objects in images using YOLOv3 (You Only Look Once) with OpenCV and Python. The model is trained on the COCO dataset, capable of detecting over 80 object categories such as people, vehicles, animals, and various household items.
📌 This project is intended for educational and learning purposes.
🗂 Project Information
Project Name | Language Used | Developer |
---|---|---|
YOLO Object Detection with OpenCV | Python | UPDATEGADH |
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⚙️ Installation Instructions
Install the required Python libraries before running the script:
pip install numpy
pip install opencv-python
pip install imutils
🧠 Technology Stack
- Programming Language: Python
- Libraries Used: OpenCV, NumPy, Imutils
- Model: YOLOv3 (Pre-trained on COCO dataset)
- No Database Required
🧰 Available Features
✅ Object detection using YOLOv3
✅ Detection in still images
✅ Supports 80+ COCO object classes
✅ Lightweight and fast
✅ Offline execution
✅ No database setup needed
✅ Easy to customize and extend
✅ Clean, modular Python code
⚠️ Limitations
- Less effective for small objects or objects placed closely together.
- Based on YOLO’s grid prediction system, which may skip multiple small objects in one grid cell.
- Consider Faster R-CNN or SSD for use cases involving small or densely packed objects.
💬 Final Words
This is a premium educational project suitable for students, researchers, and AI enthusiasts who want to explore object detection using deep learning. It’s simple to deploy, requires no database, and uses open-source Python libraries.
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