Embedded Deployment for Real-Time Road Sign Detection and Identification

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.

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Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1109/argencon62399.2024.10735925
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
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article

Embedded Deployment for Real-Time Road Sign Detection and Identification

Pablo F. Dávila, Alberto Sánchez, Xavier Casanova
Autonomous Vehicle Technology and Safety
article

Embedded Deployment for Real-Time Road Sign Detection and Identification

Pablo F. Dávila, Alberto Sánchez, Xavier Casanova
article en

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.

Universidad San Francisco de Quito (EC)
Sustainable cities and communities
Openalex Percentile: Top 80%
Autonomous Vehicle Technology and Safety
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