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An Uncertainty Estimation Framework for Probabilistic Object Detection
[article]
2021
arXiv
pre-print
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,
arXiv:2106.15007v1
fatcat:rizbb5slfvbxlpley7e7x4c22q