An Uncertainty Estimation Framework for Probabilistic Object Detection

In this paper, we introduce a new technique that combines two popular methods to estimate uncertainty in object detection. Quantifying uncertainty is critical in real-world robotic applications. Traditional detection models can be ambiguous even when they provide a high-probability output. Robot act...

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Bibliographic Details
Published inarXiv.org
Main Authors Lyu, Zongyao, Gutierrez, Nolan B, Beksi, William J
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.06.2021
Subjects
Object recognition
Robotics
Statistical analysis
Uncertainty
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Summary:In this paper, we introduce a new technique that combines two popular methods to estimate uncertainty in object detection. Quantifying uncertainty is critical in real-world robotic applications. Traditional detection models can be ambiguous even when they provide a high-probability output. Robot actions based on high-confidence, yet unreliable predictions, may result in serious repercussions. Our framework employs deep ensembles and Monte Carlo dropout for approximating predictive uncertainty, and it improves upon the uncertainty estimation quality of the baseline method. The proposed approach is evaluated on publicly available synthetic image datasets captured from sequences of video.
ISSN:2331-8422