Built a real-time object detection system using AI and pre-trained models.
This project focused on implementing an AI-powered object detection system using the COCO dataset and
pre-trained models. It identifies and labels objects from a live video stream or static images with
high accuracy and responsiveness.learned along the way.
Object Detection Using AI
Tech Stack:
Features:
Python
OpenCV
TensorFlow / PyTorch
COCO Dataset (Common Objects in Context)
Jupyter Notebook for training & testin
Real-time object detection via webcam or uploaded images
Bounding box overlays with class names and confidence scores
Lightweight implementation for fast prototyping
Adaptable to edge devices or browser integration with further optimization
Can be scaled to domain-specific detection (e.g., security, health)
Used real-time camera input to test object recognition on various indoor and outdoor scenes. Focused on accuracy, model tuning,
and UI overlays for clear object labeling. Explored use cases for smart surveillance, retail analytics, and assistive tech.
