Master Thesis Online
Master thesis : Deep Learning for Automatic Traffic Sign Inventory on an Embedded Device
Cabay, Jean-Philippe 
Promotor(s) :
Geurts, Pierre
Date of defense : 5-Sep-2022/6-Sep-2022 • Permalink : http://hdl.handle.net/2268.2/16307
Master_Thesis_Jean-Philippe_Cabay_NTT.pdfDetails
| Title : | Master thesis : Deep Learning for Automatic Traffic Sign Inventory on an Embedded Device |
| Author : | Cabay, Jean-Philippe
|
| Date of defense : | 5-Sep-2022/6-Sep-2022 |
| Advisor(s) : | Geurts, Pierre
|
| Committee's member(s) : | Van Droogenbroeck, Marc
Louppe, Gilles
Jourdain, Frédéric |
| Language : | English |
| Discipline(s) : | Engineering, computing & technology > Computer science |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master : ingénieur civil en science des données, à finalité spécialisée |
| Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] Traffic signs play an essential role in maintaining road safety by providing drivers with important information about the road and its surroundings. The road infrastructure should encourage a safe and smooth flow of traffic and reduce the risk of evaluation and driving errors on the part of road users. This thesis will focus on the development of a Proof of Concept for an embedded
traffic sign inventory system. The solution runs in real-time and to detect, classify and estimate the precise geo-coordinates of traffic signs.
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