we will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.

🖍️ This project achieves some functions of image identification for Self-Driving Cars. First, use yolov5 for object detection. Second, image classification for traffic light and traffic sign. Furthermore, the GUI of this project makes it more user-friendly for users to realize the image identification for Self-Driving Cars.

A CNN is used to classify 43 traffic sign types from the GTSRB dataset, automating data processing and model evaluation.

This repository provides a solution for classifying traffic signs using Convolutional Neural Networks (CNN). It includes code for training, testing, and deploying the model with various files such as trained models and test scripts.

Веб приложение распознавания дорожных знаков в реальном времени

In this project, I trained a YOLOv8 model to detect various traffic signs from images. Traffic sign detection is crucial for autonomous driving systems, and YOLOv8's ability to perform real-time detection makes it an excellent choice for this task.

A Deep Neural Network to do traffic sign classification

Türkiye Trafik İşaretleri Veriseti - Turkish Traffic Sign Dataset

A traffic sign convolutional neural network trained

This project aims to develop a neural network using TensorFlow to classify traffic signs from images, utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset.

Robust Transformer with Locality Inductive Bias and Feature Normalization (JESTECH 2023)

Projects for Neural Networks course, Shahid Beheshti University, Fall 2020

An innovative traffic sign detection technique for precise and efficient identification

An intelligent traffic management system to guide traffic authorities understand the trends of traffic and predict the future traffic conditions so that they can prepare themselves. Along with this it gives many other features to improve traffic control.

This project aims to detect speed limit signs using image processing and machine learning techniques.

Deep Learning project that uses yolov5 to detect and classify traffic signals in videos.

This project is a real-time traffic sign recognition system built using Python, OpenCV, and a pre-trained CNN model, capable of detecting and recognizing traffic signs from images.

Utilizing ResNet152V2 for Traffic Sign Classification: Achieve High Accuracy in Identifying 52 Sign Types with 99% Precision