# Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty

Source: [Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty](<https://www.sota2.com/research/paper/68fa682b1e209131cea1a796>)

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

Data updated: 2025-10-25T22:31:00.251Z

Authors: Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer

Published: 2020-08-10T14:49:49.000Z

## Abstract

Reliable uncertainty estimation is crucial for robust object detection in autonomous driving. However, previous works on probabilistic object detection either learn predictive probability for bounding box regression in an un-supervised manner, or use simple heuristics to do uncertainty regularization. This leads to unstable training or suboptimal detection performance. In this work, we leverage our previously proposed method for estimating uncertainty inherent in ground truth bounding box parameters (which we call label uncertainty) to improve the detection accuracy of a probabilistic LiDAR-based object detector. Experimental results on the KITTI dataset show that our method surpasses both the baseline model and the models based on simple heuristics by up to 3.6% in terms of Average Precision.

## 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/2008.04168>)

[PDF](<https://arxiv.org/pdf/2008.04168.pdf>)