Calibrating Uncertainties in Object Localization Task

TL;DR

This paper improves the calibration of uncertainty estimates in object localization tasks, crucial for safety-critical applications, by adapting existing calibration techniques to produce more reliable bounding box confidence intervals.

Contribution

It introduces a calibration method for bounding box uncertainties in object detection, enhancing the reliability of uncertainty estimates in safety-critical scenarios.

Findings

Calibrated models provide more reliable uncertainty estimates.

Improved confidence intervals for object bounding boxes.

Enhanced safety in autonomous systems.

Abstract

In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, such modules need to estimate the probability of each predicted object in a given region and the confidence interval for its bounding box. While recent Bayesian deep learning methods provide a principled way to estimate this uncertainty, the estimates for the bounding boxes obtained using these methods are uncalibrated. In this paper, we address this problem for the single-object localization task by adapting an existing technique for calibrating regression models. We show, experimentally, that the resulting calibrated model obtains more reliable uncertainty estimates.