Training a convolutional neural network for transportation sign detection using synthetic dataset

2021 ◽  
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Saide Lo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Synthetic Dataset ◽  
Sign Detection

An efficient convolutional neural network for small traffic sign detection

2019 ◽  
Vol 97 ◽  
pp. 269-277 ◽  
Author(s):  
Shijin Song ◽  
Zhiqiang Que ◽  
Junjie Hou ◽  
Sen Du ◽  
Yuefeng Song
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

Ecuadorian traffic sign detection through color information and a convolutional neural network

2020 ◽  
Author(s):  
Marco Flores-Calero ◽  
Gonzalo Espinel ◽  
Jose Carrillo-Medina ◽  
Patricio Vizcaino ◽  
Marco Gualsaqui ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Color Information ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

A Robust System for Road Sign Detection and Classification Using LeNet Architecture Based on Convolutional Neural Network

2020 ◽  
pp. 57-75
Author(s):  
Amal Bouti ◽  
Mohamed Adnane Mahraz ◽  
Jamal Riffi ◽  
Hamid Tairi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Accuracy Rate ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Sign Detection ◽  
Robust System

In this chapter, the authors report a system for detection and classification of road signs. This system consists of two parts. The first part detects the road signs in real time. The second part classifies the German traffic signs (GTSRB) dataset and makes the prediction using the road signs detected in the first part to test the effectiveness. The authors used HOG and SVM in the detection part to detect the road signs captured by the camera. Then they used a convolutional neural network based on the LeNet model in which some modifications were added in the classification part. The system obtains an accuracy rate of 96.85% in the detection part and 96.23% in the classification part.

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