Uncertainty in Real-Time Semantic Segmentation on Embedded Systems

• URL: http://arxiv.org/abs/2301.01201v4
• Date: Mon, 31 Jul 2023 08:03:57 GMT
• Title: Uncertainty in Real-Time Semantic Segmentation on Embedded Systems
• Authors: Ethan Goan, Clinton Fookes
• Abstract summary: 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. This paper addresses this by combining deep feature extraction from pre-trained models with Bayesian regression and moment propagation for uncertainty aware predictions.
• Score: 22.018605089162204
• License: http://creativecommons.org/licenses/by/4.0/
• 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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