An Uncertainty Estimation Framework for Probabilistic Object Detection [article]

Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi
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,
more » ... nd 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.
arXiv:2106.15007v1 fatcat:rizbb5slfvbxlpley7e7x4c22q