Paper Info
Title
Real-Time Implementation Of Traffic Signs Detection And Identification Application On Graphics Processing Units
Abstract
Traffic signs detection has become an important feature of Advanced driving assisting systems and even self-driving cars. In this paper, we present an implementation of a traffic signs detection method on Graphics Processing Units (GPU) under real-time conditions. The proposed model is based on deep convolutional neural networks, a deep learning model used in computer vision applications. The deep convolutional neural networks have recently been used to solve many computer vision tasks successfully. Unlike old techniques, the model is used to detect and identify the traffic signs at the same time without the need for any external modules. To achieve real-time inference, we implement the proposed model on the GPU as a natural choice for the implementation of deep learning-based models. Also, we build large traffic signs detection dataset. The dataset contains 10000 images captured from the Chinese roads under real-world factors like lightning, occlusion, complex background, etc. 73 traffic sign classes were considered in this dataset. The evaluation of the proposed model on the proposed dataset shows robust performance in terms of speed and accuracy.
Year
DOI
Venue
2021
10.1142/S0218001421500245
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Real-time processing, traffic signs detection, deep learning, high-performance computing application, convolutional neural networks
Journal
35
Issue
ISSN
Citations 
07
0218-0014
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Riadh Ayachi1153.46
Mouna Afif293.28
Yahia Said3217.09
Abdessalem Ben Abdelaali430.77