AI/ML
vehicle detection in an video using yolo and python
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A Python-based application that uses YOLO to detect and track vehicles in video streams in real-time.
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₹2999.00
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Category
AI/ML
Difficulty
Intermediate
Languages
Python
Tech Stack
5 technologies
Features
8 included
Updated
16 Feb 2026
About vehicle detection in an video using yolo and python
This project implements a vehicle detection system using the YOLO (You Only Look Once) object detection algorithm with Python. It processes video files or live camera feeds to identify and track various vehicles like cars, trucks, and motorcycles. The application provides real-time detection with bounding boxes and confidence scores, making it suitable for traffic monitoring and surveillance applications.
Key Features
- 1Real-time vehicle detection using YOLOv5 model
- 2Support for video file input and live camera feed
- 3Bounding box visualization with class labels and confidence scores
- 4Vehicle tracking across frames using simple tracking algorithm
- 5Export detection results to CSV or JSON format
- 6Configurable detection threshold and non-maximum suppression
- 7Performance metrics display (FPS, detection count)
- 8Command-line interface for easy usage
Tech Stack
PythonOpenCVYOLOv5PyTorchNumPy
Programming Languages
Python
Installation Steps
- 1
Clone the repository from GitHub - 2
Create a virtual environment: python -m venv venv - 3
Activate the virtual environment - 4
Install dependencies: pip install -r requirements.txt - 5
Download YOLOv5 weights file - 6
Configure the config.yaml file with your paths - 7
Run the detection script: python detect.py --source video.mp4 - 8
View results in the output folder
Prerequisites
Prerequisites- Python 3.8+
- pip package manager
- FFmpeg (for video processing)
- CUDA-enabled GPU (optional for faster processing)
- Basic understanding of Python and object detection concepts
#vehicle-detection #yolo #computer-vision #python #real-time-processing #traffic-monitoring #object-detection #opencv
