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2019 | OriginalPaper | Buchkapitel

CNN Based Traffic Sign Recognition for Mini Autonomous Vehicles

verfasst von : Yusuf Satılmış, Furkan Tufan, Muhammed Şara, Münir Karslı, Süleyman Eken, Ahmet Sayar

Erschienen in: Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018

Verlag: Springer International Publishing

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Abstract

Advanced driving assistance systems (ADAS) could perform basic object detection and classification to alert drivers for road conditions, vehicle speed regulation, and etc. With the advances in the new hardware and software platforms, deep learning has been used in ADAS technologies. Traffic signs are an important part of road infrastructure. So, it is very important task to detect and classify traffic signs for autonomous vehicles. In this paper, we firstly create a traffic sign dataset from ZED stereo camera mounted on the top of Racecar mini autonomous vehicle and we use Tiny-YOLO real-time object detection and classification system to detect and classify traffic signs. Then, we test the model on our dataset in terms of accuracy, loss, precision and intersection over union performance metrics.

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Vorheriges Kapitel On Transforming Unit Cube into Tree by One-Point Mutation
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Metadaten
Titel
CNN Based Traffic Sign Recognition for Mini Autonomous Vehicles
verfasst von
Yusuf Satılmış
Furkan Tufan
Muhammed Şara
Münir Karslı
Süleyman Eken
Ahmet Sayar
Copyright-Jahr
2019
Buch
Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018

Print ISBN: 978-3-319-99995-1

Electronic ISBN: 978-3-319-99996-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-99996-8_8