Code for the paper entitled "Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods".

Reproduce GTSRB results of classic deep learning papers.

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.

Detect traffic sign and recognize them using Image Processing algorithms and Machine Learning(Random Forest)

Traffic sign detection and classification

GTSRB - German Traffic Sign Recognition

Detection and recognition of traffic signs.

Fast and accurate ResNet for the GTSRB dataset

Dieses Projekt beschäftigt sich mit der Entwicklung eines flachen CNN zur Erkennung von Verkehrsschildern. Das Projekt beinhaltet alle dazu benötigten Programme und Tools.

A traffic sign classifier using LeNet for Self driving cars

🚸⛔Novel Deep Convolutional Network is proposed for traffic sign classification that achieves outstanding performance on GTSRB surpassing the best human performance of 98.84%.

Deep Learning for Autonomous Driving - Laboratory

An implementation of CS50's AI project using computer vision to determine the type of traffic signs in photos.

Road traffic sign recognition and detection with use of OpenCV, ROS and Arduino build up robot

Traffic Sign Classification - GTSRB dataset

We build a traffic sign classifier with multi-scale Convolutional Networks using Keras

Classify traffic signs by using the AlexNet and GoogLeNet architecture using GTSRB dataset and comparing the two

A CNN model to classify German traffic signs

Image Scaling Attack on German Traffic Sign Recognition Benchmark CNN Model.

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