Uncertainty in Real-Time Semantic Segmentation on Embedded Systems

Ethan Goan; Clinton Fookes

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arXiv · 2301.01201

Uncertainty in Real-Time Semantic Segmentation on Embedded Systems

Ethan Goan·Clinton Fookes

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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.

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Ethan Goan, Clinton Fookes. 2022-12-20. Uncertainty in Real-Time Semantic Segmentation on Embedded Systems. https://arxiv.org/abs/2301.01201

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