🚠 Hazrat Ali

{ CEO and Founder ✈ HMSoftTecH Innovation 🛫 } { 🚁 Researcher | 💻 Programmer | 🚀 Entrepreneur | 🛸Businessman }

🚗 Vehicle Detection & Counting Using YOLO and Masking — Intelligent Traffic Monitoring

Vehicle-Detection-Counting-Using-YOLO-and-Masking is a computer vision project that automatically detects, tracks, and counts vehicles from images or video streams using YOLO-based object detection combined with region masking techniques.

The system focuses on accurate vehicle detection within specific regions of interest, enabling efficient traffic monitoring, congestion analysis, and smart city applications.

This project demonstrates real-world implementation of deep learning, object detection, and traffic analytics systems.

✨ Key Features

🚘 Real-Time Vehicle Detection

Detect cars, buses, trucks, and motorcycles

Powered by YOLO object detection models

🎯 Region of Interest (ROI) Masking

Apply masks to focus detection on specific road areas

Reduce noise and false detections

🔢 Vehicle Counting System

Count vehicles passing through defined zones

Track traffic flow statistics

🎥 Video Stream Processing

Works with CCTV footage or recorded videos

Real-time frame-by-frame analysis

📊 Traffic Data Insights

Generate traffic counts and flow patterns

Useful for traffic optimization studies

🧰 Tech Stack

Language: Python

Libraries: OpenCV, NumPy

Deep Learning Framework: PyTorch / TensorFlow

Model: YOLO (You Only Look Once)

Tools: Jupyter Notebook, Git

🎯 Project Objectives

Implement YOLO-based object detection for traffic monitoring

Apply masking techniques for precise vehicle tracking

Build a smart traffic analysis system

Demonstrate real-world computer vision applications

🌟 Ideal For

🤖 Computer Vision & AI Engineers

🚦 Smart city and traffic management systems

🎓 Machine learning students and researchers

💼 AI portfolio and research projects