@inproceedings{f92ff66863dd427f8dc1e53c49b8c479,
title = "Embedded Deployment for Real-Time Road Sign Detection and Identification",
abstract = "This article reports on the design and deployment of a Deep Neural Network (DNN) in a hardware platform for real-time detection and identification of road signs. A YOLOv3 DNN is trained using the Keras framework and deployed into an AMD Kria KV260 development board using the Vivado, Vitis IDE and Vitis AI tools. The DNN is implemented in a Deep Learning Processing Unit (DPU) core and the software application is developed at an operating system level using the Python Productivity for Zynq (PYNQ) framework from AMD.",
keywords = "KV-260, Keras, Pynq, Vitis-AI, YOLOv3",
author = "Xavier Casanova and Alberto S{\'a}nchez and D{\'a}vila, {Pablo F.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 7th IEEE Biennial Congress of Argentina, ARGENCON 2024 ; Conference date: 18-09-2024 Through 20-09-2024",
year = "2024",
doi = "10.1109/ARGENCON62399.2024.10735925",
language = "Ingl{\'e}s",
series = "2024 7th IEEE Biennial Congress of Argentina, ARGENCON 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 7th IEEE Biennial Congress of Argentina, ARGENCON 2024",
}