Introduction :

Real-Time Phone Usage Detection with YOLOv10 Welcome to the Real-Time Phone Usage Detection repository! This project leverages the power of YOLOv10, a state-of-the-art object detection model, to detect and monitor phone usage by individuals in real-time using a webcam feed. The system is designed to identify when a person is using a phone by analyzing the spatial relationship between detected persons and phones in the video stream.

This repository provides a complete implementation, including:

Real-time object detection using YOLOv10.

Overlap detection between person and phone bounding boxes.

Customizable video capture resolution for optimal performance.

Easy-to-use scripts for deployment and testing.

Whether you're building a surveillance system, a productivity monitoring tool, or just exploring computer vision applications, this project serves as a robust starting point for detecting phone usage in real-time

Key Features :

• Real-Time Detection: Detects phone usage in live video streams with high accuracy.
• Customizable Resolution: Supports adjustable video capture resolution for compatibility with various cameras.
• Overlap Detection: Uses bounding box overlap logic to determine if a person is using a phone.
• Lightweight and Efficient: Built with YOLOv10 for fast and efficient inference.
• Easy to Use: Simple setup and deployment process with detailed instructions.

Uses Case :

• Surveillance Systems: Monitor phone usage in restricted areas.
• Productivity Monitoring: Track phone usage in workplaces or classrooms.

Installation :

Clone the repository:

git clone https://github.com/Khaledabdsalame/Person_Use_Phone_Detector.git
cd Person_Use_Phone_Detector

Install:

pip install -r requirements.txt

Usage:

• Run the script to start real-time detection:

python OpenCv_Phone_Detector.py

• Adjust the video capture resolution in the script if needed.
• Press q to exit the application.

Licence :

MIT License Copyright (c) Khaled Abdsalame From CSA Team