  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.
