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Real Time Object Detection of Traffic Signs and Lights with ROS

  • A real time object detection system is an essential core feature of any autonomous driving car. With the variety of models and data sets available, there is great opportunity to train a model that meets the high requirements of autonomous vehicle. In this paper we want to present an object detection model for traffic signs and lights implemented on a NVIDIA Xavier board [7] using the robot operating system (ROS) [9]. Furthermore we explain how to create and evaluate a data set as well as to test the accuracy and performance of the implemented detector. In regards to pattern recognition with neural networks, the performance and accuracy must also be weighed up. The goal is to achieve one without sacrificing the other. Considering all these aspects, we decided to train a YOLOv4 Standard and a YOLOv4 Tiny configuration [2]. While the accuracy of the YOLOv4 network exhibited a very high accuracy rate on high and mediocre resolutions but the performance is too poor to be used in a real time object detection system. On the other hand, theA real time object detection system is an essential core feature of any autonomous driving car. With the variety of models and data sets available, there is great opportunity to train a model that meets the high requirements of autonomous vehicle. In this paper we want to present an object detection model for traffic signs and lights implemented on a NVIDIA Xavier board [7] using the robot operating system (ROS) [9]. Furthermore we explain how to create and evaluate a data set as well as to test the accuracy and performance of the implemented detector. In regards to pattern recognition with neural networks, the performance and accuracy must also be weighed up. The goal is to achieve one without sacrificing the other. Considering all these aspects, we decided to train a YOLOv4 Standard and a YOLOv4 Tiny configuration [2]. While the accuracy of the YOLOv4 network exhibited a very high accuracy rate on high and mediocre resolutions but the performance is too poor to be used in a real time object detection system. On the other hand, the YOLOv4 tiny network reached the strived for performance even on the highest resolution scales tested, but at the cost of accuracy.show moreshow less

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Metadaten
Author:Marius Bertele, Christian Dindorf
DOI:https://doi.org/10.13140/RG.2.2.16743.32163
Parent Title (German):Technical Reports in Computing Science
Series (Serial Number):Technical Reports in Computing Science – Schriftenreihe Informatik der HS Kempten (July 2021)
Publisher:Hochschule Kempten, Fakultät Informatik
Place of publication:Kempten
Document Type:Preprint
Language:English
Year of first Publication:2021
Publishing Institution:Hochschule für angewandte Wissenschaften Kempten
Issue:July 2021
Number of pages:5 Seiten
First Page:1
Last Page:5
Institutes:Fakultät Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme
Open Access:open_access
Research focus:FSP2: Mobilität
Publication reviewed:nicht begutachtet
Release Date:2023/03/01
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