Fast-tracking application for Traffic Signs Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F18%3A73587378" target="_blank" >RIV/61989592:15310/18:73587378 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/content/pdf/10.1007%2F978-3-030-00692-1_34.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007%2F978-3-030-00692-1_34.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-00692-1_34" target="_blank" >10.1007/978-3-030-00692-1_34</a>

Result language

angličtina

Original language name

Fast-tracking application for Traffic Signs Recognition

Original language description

Traffic sign recognition is among the major tasks on driver assistance system. The convolutional neural networks (CNN) play an important role to find a good accuracy of traffic sign recognition in order to limit the dangerous acts of the driver and to respect the road laws. The accuracy of the Detection and Classification determines how powerful of the technique used is. Whereas SSD Multibox (Single Shot MultiBox Detector) is an approach based on convolutional neural networks paradigm, it is adopted in this paper, firstly because we can rely on it for the real-time applications, this approach runs on 59 FPS (frame per second). Secondly, in order to optimize difficulties in multiple layers of DeeperCNN to provide a finer accuracy. Moreover, our experiment on German traffic sign recognition benchmark (GTSRB) demonstrated that the proposed approach could achieve competitive results (83.2% in 140.000 learning steps) using GPU parallel system and Tensorflow.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EE2.3.20.0170" target="_blank" >EE2.3.20.0170: Building of Research Team in the Field of Environmental Modeling and the Use of Geoinformation Systems with the Consequence in Participation in International Networks and Programs</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Computer Science - Proceedings of the International Conference on Computer Vision and Graphics ICCVG 2018

ISBN

978-3-030-00692-1

ISSN

—

e-ISSN

neuvedeno

Number of pages

12

Pages from-to

385-396

Publisher name

Springer

Place of publication

Heidelberg

Event location

Warszawa

Event date

Sep 17, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—