An Uncertainty Estimation Framework for Probabilistic Object Detection

TL;DR

This paper presents a novel framework combining deep ensembles and Monte Carlo dropout to improve uncertainty estimation in probabilistic object detection, crucial for safe robotic decision-making.

Contribution

It introduces a new uncertainty estimation framework that enhances existing methods by integrating deep ensembles and Monte Carlo dropout for better predictive confidence.

Findings

Improved uncertainty estimation over baseline methods

Effective on synthetic video datasets

Enhances safety in robotic applications

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.