Real-Time Object Detection System

🚀 Real-time object detection web application built using Flask, OpenCV, and YOLOv8, enabling live webcam-based detection and image inference with high-speed computer vision processing and real-time object recognition.

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Features

• Real-time object detection using YOLOv8
• Live webcam inference and streaming
• Image upload and detection functionality
• Bounding box visualization with confidence detection
• Flask-based backend server
• OpenCV integration for image and video processing
• Secure file upload handling
• Fast inference with optimized object detection workflows

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Tech Stack

• Python
• Flask
• OpenCV
• YOLOv8
• Ultralytics
• NumPy
• HTML
• CSS
• JavaScript

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Project Structure

project/
│
├── uploads/
├── templates/
│   └── index.html
├── app.py
├── requirements.txt
└── README.md

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Installation

Clone Repository

git clone <repository-url>
cd object-detection-system

Create Virtual Environment

python -m venv venv

Activate Virtual Environment

Windows

venv\Scripts\activate

Linux / macOS

source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

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Run Application

python app.py

Open browser:

http://127.0.0.1:5000

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Functionalities

• Upload image for object detection
• Real-time webcam object detection
• Automatic object annotation and visualization
• Live frame streaming with detected objects
• Multi-object recognition using YOLOv8

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Performance Optimizations

• Efficient frame processing using OpenCV
• Lightweight YOLOv8 model for faster inference
• Optimized image encoding and streaming workflows
• Real-time detection pipeline for low-latency processing

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Future Improvements

• Custom-trained YOLO models
• Object tracking integration
• GPU acceleration support
• Detection analytics dashboard
• Video file upload support

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Author

Developed by Puppala Madhuvenu

Backend-Focused Full Stack Developer