This Python-based project features an intuitive graphical user interface (GUI) facilitating image uploads for the identification of essential road signs prevalent on streets, including 20kmph, 30kmph, 50kmph, stop, and right turn signs.

Yolo11 trained on Polish traffic signs (Warsaw and vicinity)

visit the live site hosted on Heroku.com

This project implements a neural network using TensorFlow to classify images of traffic signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The model accurately identifies different types of traffic signs, such as stop signs, speed limit signs, and yield signs, among others.

A deep learning project implementing a Convolutional Neural Network (CNN) in TensorFlow to classify German traffic signs. Features parallel image processing, custom model architecture, and 95%+ accuracy on the GTSRB dataset. Built as part of Harvard's CS50 AI course.

It's a simple python script to predict the traffic signs

A deep learning model that classifies traffic signs using the German Traffic Sign dataset to assist autonomous vehicles in recognizing road signs.

Traffic Sign Detection with SIFT (Scale-Invariant Feature Transform)

A Deep Learning Traffic Sign Recognition System

Open source neural network solutions for the GTSRB challenge

The Road Sign Recognition project is a real-time detection system designed to recognize road signs across 43 different classes. The project leverages the YOLOv5 model, which is trained on the GTSRB - German Traffic Sign Recognition Benchmark dataset.

implementation of Traffic Sign Recognition on a driving dataset using Python3

Traffic sign detection and classification application developed in the Computer Vision (VCOM) class.

A deep learning model to classify Traffic Signals.

PBL project for SE

traffic sign recognition using CNN

Detect and recognise traffic lights using Hough circle transform implemented with OpenCV and Python

Modified yolov3 is employed to detect traffic signs.