In recent years, real-time semantic segmentation on embedded devices has become increasingly popular, largely driven by the growing interest in smart vehicles and robots. The rising of autonomous driving has brought about new challenges for these systems, such as the need for low latency and computation-intensive operations, which can lead to excessive energy consumption and computing power. To address these challenges, this paper focuses on the critical task of semantic segmentation, which is essential for accurate environment perception, and proposes an implementation that achieves high accuracy and low complexity using a U-Net as base architecture. The goal is to enable real-time semantic segmentation on low-power cores while preserving performance, which is crucial for the success of autonomous vehicles and robots. The lightweight U-Net architectures have been implemented in a STM32 microcontroller, namely STM32L4R5, as a severe benchmark to meet the low-power, low-cost requirements.

An U-Net Semantic Segmentation Vision System on a Low-Power Embedded Microcontroller Platform / Falaschetti, Laura; Bruschi, Sara; Alessandrini, Michele; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - ELETTRONICO. - 225:(2023), pp. -4482. (Intervento presentato al convegno International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2023) tenutosi a Athens, Greece nel 06-08 September 2023) [10.1016/j.procs.2023.10.445].

An U-Net Semantic Segmentation Vision System on a Low-Power Embedded Microcontroller Platform

Falaschetti, Laura
;
Bruschi, Sara;Alessandrini, Michele;Biagetti, Giorgio;Crippa, Paolo;Turchetti, Claudio
2023-01-01

Abstract

In recent years, real-time semantic segmentation on embedded devices has become increasingly popular, largely driven by the growing interest in smart vehicles and robots. The rising of autonomous driving has brought about new challenges for these systems, such as the need for low latency and computation-intensive operations, which can lead to excessive energy consumption and computing power. To address these challenges, this paper focuses on the critical task of semantic segmentation, which is essential for accurate environment perception, and proposes an implementation that achieves high accuracy and low complexity using a U-Net as base architecture. The goal is to enable real-time semantic segmentation on low-power cores while preserving performance, which is crucial for the success of autonomous vehicles and robots. The lightweight U-Net architectures have been implemented in a STM32 microcontroller, namely STM32L4R5, as a severe benchmark to meet the low-power, low-cost requirements.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/325152
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