The Speed Limit Sign Detection is a Computer Vision project that combines Result dashboard and Non-max suppression, built with Streamlit UI. The project follows a clean, modular pipeline where image input, processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical Computer Vision techniques while producing a working, demo-ready application.
Manual visual inspection and analysis of this problem is slow, error-prone, and impossible to scale. Without a Computer Vision approach built on Non-max suppression and Streamlit UI, users cannot automatically detect, classify, or track visual patterns, and there is no reliable way to evaluate accuracy.
This project applies Computer Vision techniques through Result dashboard, orchestrated with Streamlit UI and Non-max suppression. The pipeline is designed for accuracy and speed, with preprocessing, model inference, and clear evaluation. It produces consistent, reusable results and can be adapted to related vision tasks with minimal changes.
Streamlit UI
OpenCV
Python 3.11+
Deep learning frameworks
GPU / Google Colab
YOLO
Modular vision pipeline around Result dashboard and Non-max suppression
Configurable model and preprocessing settings
Clear logging, metrics, and error handling
Clean interface for viewing results
Reusable components for related vision tasks
Real-time or batch inference support
The project is layered: the input layer loads and preprocesses images through Result dashboard; the inference layer runs the vision model with Streamlit UI and Non-max suppression; and the output layer formats and presents results via Real-time video inference. Shared configuration, logging, and evaluation modules support all layers, keeping the system robust and easy to extend.
Set up the environment, datasets, and configuration files.
Build the image loading and preprocessing layer with Result dashboard.
Implement the core vision pipeline using Streamlit UI and Non-max suppression.
Add the output and presentation layer via Real-time video inference.
Wire up end-to-end flows and add error handling and logging.
Train or tune the model, run evaluation, and refine results.
Package the project, document it, and prepare the demo and viva report.
Build production-style Computer Vision applications
Apply Tuning detection confidence and Building detection applications
Preprocess and analyze real image data
Work with popular vision libraries and models
Present and defend a complete Computer Vision project in viva
Expose the pipeline as a REST API for other apps
Add more classes and larger training data
Add edge deployment for mobile devices
Optimize inference for real-time speed
The Speed Limit Sign Detection delivers a complete Computer Vision workflow — from image input and processing to analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand vision skills.
