Traffic signs detection and classification in real time

Türkiye Trafik İşaretleri Veriseti - Turkish Traffic Sign Dataset

To ease the driver to identify the Traffic Signs and also for the efficient working of Self-Driving Cars.

In this project, a traffic sign recognition system, divided into two parts, is presented. The first part is based on classical image processing techniques, for traffic signs extraction out of a video, whereas the second part is based on machine learning, more explicitly, convolutional neural networks, for image labeling.

A traffic sign classifier built with TensorFlow

Synthetic traffic sign detectron

Objects recognition and classification using machine learning, computer vision and real-time object detection algorithm

My work on CS50's Introduction to Artificial Intelligence with Python.

Cuộc đua số (2017 -2018) University Round - Detect and Recognize Traffic Signs using OpenCV and Machine Learning

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

Modified yolov3 is employed to detect traffic signs.

Developing the mobile app in Python using Kivy, integrating TensorFlow Lite and OpenCV for real-time processing. | Python ve Kivy ile mobil uygulama geliştirme, TensorFlow Lite ve OpenCV entegrasyonu.

This project accompanies the lecture deep learning and handles the GTSRB dataset. Neural networks are fooled by the help of popular adversarial attacks.

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.