Object Detection

This project demonstrates the power of real-time object detection using the YOLOv8 model. It is designed to detect and classify objects with remarkable accuracy and speed, making it suitable for a wide range of applications.

Key Features

Real-time detection of multiple objects with high precision.
Flexibility for custom training using the COCO dataset or your own dataset.
Seamless integration with Flask API for web-based control and accessibility.
Support for edge devices, allowing deployment on low-power systems.
Scalable architecture for handling large-scale deployments in production environments.

Technologies Used

The following technologies were used in this project:

YOLOv8: State-of-the-art object detection model for accurate and efficient performance.
Flask: Lightweight web framework to provide API endpoints and control functionalities.
OpenCV: Library for image and video processing, enabling real-time frame capture and display.
Python: Programming language for seamless integration of libraries and development of backend logic.

How It Works

The camera captures video frames and sends them to the YOLOv8 model for processing.
The model identifies and classifies objects in real-time, drawing bounding boxes around detected objects.
Processed frames are displayed on the user interface, allowing users to view detection results instantly.
Users can start or stop the detection process via a web-based interface controlled by the Flask API.

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