scholarly journals Modification of the Pre-Processing Stage of a Traffi Sign Recognition System Taking into Account the Characteristics of the Subject Area

Vestnik NSUEM ◽  
2020 ◽  
pp. 235-249
Author(s):  
S. Yu. Pchelintsev
Keyword(s):  
Processing Speed ◽  
Recognition System ◽  
Subject Area ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Road Signs ◽  
Sign Recognition ◽  
The Subject ◽  
Recognition Systems ◽  
High Level

Traffic sign recognition systems require a high level of responsiveness and accuracy with limited use of computing resources. The process of image pre-processing precedes the process of directly recognizing images, therefore, the recognition results depend on its effectiveness. When conducting pre-processing, it is important to take into account the features of the subject area, within which recognition is performed. The article discusses the process of pre-processing and preparing images in the context of creating a system for recognizing road signs. The main problems that arise during the operation of such a system are identified. Their solutions are proposed. Own combination of these solutions allowed us to create a new system for recognizing road signs, which gives a gain in processing speed by cutting off images of no interest before entering the classifier, and also taking into account the peculiarities of operation in an urban environment – more difficult conditions compared with recognition of road signs on tracks or on artificially created training grounds.

scholarly journals A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet-5 Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ameur Zaibi ◽  
Anis Ladgham ◽  
Anis Sakly
Keyword(s):  
Deep Learning ◽  
Research Work ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Data Set ◽  
Road Signs ◽  
Sign Recognition ◽  
Road Sign ◽  
Recognition Systems ◽  
Embedded Application

For several years, much research has focused on the importance of traffic sign recognition systems, which have played a very important role in road safety. Researchers have exploited the techniques of machine learning, deep learning, and image processing to carry out their research successfully. The new and recent research on road sign classification and recognition systems is the result of the use of deep learning-based architectures such as the convolutional neural network (CNN) architectures. In this research work, the goal was to achieve a CNN model that is lightweight and easily implemented for an embedded application and with excellent classification accuracy. We choose to work with an improved network LeNet-5 model for the classification of road signs. We trained our model network on the German Traffic Sign Recognition Benchmark (GTSRB) database and also on the Belgian Traffic Sign Data Set (BTSD), and it gave good results compared to other models tested by us and others tested by different researchers. The accuracy was 99.84% on GTSRB and 98.37% on BTSD. The lightness and the reduced number of parameters of our model (0.38 million) based on the enhanced LeNet-5 network pushed us to test our model for an embedded application using a webcam. The results we found are efficient, which emphasize the effectiveness of our method.

scholarly journals Analysis of Market-Ready Traffic Sign Recognition Systems in Cars: A Test Field Study

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3697
Author(s):  
Darko Babić ◽  
Dario Babić ◽  
Mario Fiolić ◽  
Željko Šarić
Keyword(s):  
Recognition System ◽  
Test Field ◽  
Traffic Sign Recognition ◽  
Driver Assistance ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection ◽  
Recognition Systems ◽  
Safety Systems ◽  
Different Levels

Advanced Driver Assistance System (ADAS) represents a collection of vehicle-based intelligent safety systems. One in particular, Traffic Sign Recognition System (TSRS), is designed to detect and interpret roadside information in the form of signage. Even though TSRS has been on the market for more than a decade now, the available ones differ in hardware and software solutions they use, as well as in quantity and typology of signs they recognize. The aim of this study is to determine whether differences between detection and readability accuracy of market-ready TSRS exist and to what extent, as well as how different levels of “graphical changes” on the signs affect their accuracy. For this purpose, signs (“speed limit” and “prohibition of overtaking”) were placed on a test field and 17 vehicles from 14 different car brands underwent testing. Overall, the results showed that sign detection and readability by TSRS differ between car brands and that even small changes in the design of signs can drastically affect TSRS accuracy. Even in a controlled environment where no sign has been altered, there has been a 5% margin of misread signs.

An Approach of Traffic Sign Recognition Algorithm on MATLAB

2014 ◽  
Vol 644-650 ◽  
pp. 3980-3983
Author(s):  
Jia Yang Li ◽  
Mei Xia Song
Keyword(s):  
Real Time ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

Traffic sign recognition system for inventory purposes

2008 ◽  
Author(s):  
S. Maldonado-Bascon ◽  
S. Lafuente-Arroyo ◽  
P. Siegmann ◽  
H. Gomez-Moreno ◽  
F.J. Acevedo-Rodriguez
Keyword(s):  
Recognition System ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition
Sign in / Sign up

Export Citation Format

Share Document