# An Uncertainty Estimation Framework for Probabilistic Object Detection

Source: [An Uncertainty Estimation Framework for Probabilistic Object Detection](<https://www.sota2.com/research/paper/68fa6a441e209131cea272b2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:21:36.715Z

Authors: Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi

Published: 2021-06-28T22:29:59.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.15007>)

[PDF](<https://arxiv.org/pdf/2106.15007.pdf>)