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.
Authors
- Pablo F. Dávila
- Alberto Sánchez (ORCID: https://orcid.org/0000-0002-1294-3044)
- Xavier Casanova
Institutions
- Universidad San Francisco de Quito (EC)
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
- 0.00