A Robust System for Road Sign Detection and Classification Using LeNet Architecture Based on Convolutional Neural Network-Reference-Cited by-同舟云学术

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

Published:2020 Issue: Volume: Page:57-75
ISSN:2327-0411
Container-title:Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies
language:
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Author:

Bouti Amal¹,Mahraz Mohamed Adnane²^ORCID,Riffi Jamal¹,Tairi Hamid¹

Affiliation:

1. Department of Computer Sciences, Sidi Mohamed Ben Abdellah University, Morocco

2. Sidi Mohamed Ben Abdellah University, Morocco

Abstract

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.

Publisher

IGI Global

Reference20 articles.

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