# Uncertainty in Real-Time Semantic Segmentation on Embedded Systems

Source: [Uncertainty in Real-Time Semantic Segmentation on Embedded Systems](<https://www.sota2.com/research/paper/68fa79d21e209131cea7f8cc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-08T05:00:12.103Z

Authors: Ethan Goan, Clinton Fookes

Published: 2022-12-20T07:32:12.000Z

## Abstract

Application for semantic segmentation models in areas such as autonomous vehicles and human computer interaction require real-time predictive capabilities. The challenges of addressing real-time application is amplified by the need to operate on resource constrained hardware. Whilst development of real-time methods for these platforms has increased, these models are unable to sufficiently reason about uncertainty present when applied on embedded real-time systems. This paper addresses this by combining deep feature extraction from pre-trained models with Bayesian regression and moment propagation for uncertainty aware predictions. We demonstrate how the proposed method can yield meaningful epistemic uncertainty on embedded hardware in real-time whilst maintaining predictive performance.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2301.01201>)

[PDF](<https://arxiv.org/pdf/2301.01201.pdf>)