A DEEP LEARNING BASED SYSTEM DETECTS HELMET VOILATIONS AND TRIPLE RIDING
Objective
The primary goal of this project is to develop an automated system for detecting traffic violations through advanced deep learning models, specifically YOLOv8 and YOLOv11. This system will be capable of detecting and classifying a range of traffic violations, including not wearing helmets, triple riding, phone usage while riding, and wheeling. The project will involve the development of a Python-based backend to process real-time video feeds, ensuring efficient detection of violations as they occur
Abstract
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
4.2 Hardware Requirements
- Processor: Intel i3 or equivalent for handling basic video processing.
- RAM: 8 GB for smooth operation.
- Storage: 160 GB hard disk.
- Cameras:
- High-definition CCTV cameras (1080p or higher resolution).
- Frame rate: Minimum 30 frames per second (FPS).
- GPU: None specified, but NVIDIA GTX 1080 or higher is recommended for faster inference in deep learning tasks.
- Network: High-speed internet connection (minimum 1 Gbps) for smooth data transfer.
4.3 Software Requirements
- Operating System:
- Windows 7/8/10 for the front-end development.
- Linux-based OS (Ubuntu 18.04 or higher) for backend deployment.
- Server-side Script: HTML, CSS, Bootstrap, and JS.
- Programming Languages:
- Python (for backend and model training).
- JavaScript, HTML, CSS (for front-end development via Streamlit).
- Deep Learning Frameworks:
- TensorFlow or PyTorch (for YOLOv8, v11, model implementation).
- OpenCV (for video processing and frame extraction).
- Libraries and Tools:
- Streamlit (for the front-end interface).
- Flask (for API integration between backend and front-end).
- NumPy, Pandas (for data processing and analysis).
- Database: MySQL or PostgreSQL (for storing logs and metadata of violations).
- Version Control: Git for version control and collaboration; GitHub or GitLab for repository hosting and CI/CD pipelines.
- IDE/Workbench: PyCharm or VSCode for Python development.
- Server Deployment: XAMPP Server.
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
