# Uncertainty Estimation in One-Stage Object Detection

arXiv: https://arxiv.org/abs/1905.10296 · HTML: https://mlsanity.com/paper/1905.10296/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 30 of 36)

- [Evidential Deep Learning for Open Set Action Recognition](https://mlsanity.com/paper/2107.10161/) (2021)
- [Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors](https://mlsanity.com/paper/2101.05036/) (2021)
- [Probabilistic Representations for Video Contrastive Learning](https://mlsanity.com/paper/2204.03946/) (2022)
- [Uncertainty-Aware Multi-View Representation Learning](https://mlsanity.com/paper/2201.05776/) (2022)
- [Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs](https://mlsanity.com/paper/2302.02865/) (2023)
- [Uncertainty for Identifying Open-Set Errors in Visual Object Detection](https://mlsanity.com/paper/2104.01328/) (2021)
- [T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation](https://mlsanity.com/paper/2107.14447/) (2021)
- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction](https://mlsanity.com/paper/2403.07263/) (2024)
- [Monte Carlo DropBlock for Modelling Uncertainty in Object Detection](https://mlsanity.com/paper/2108.03614/) (2021)
- [Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning](https://mlsanity.com/paper/2112.00568/) (2021)
- [Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation](https://mlsanity.com/paper/2205.11047/) (2022)
- [RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses](https://mlsanity.com/paper/2403.01795/) (2024)
- [Auto-Encoding Score Distribution Regression for Action Quality Assessment](https://mlsanity.com/paper/2111.11029/) (2021)
- [Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors](https://mlsanity.com/paper/2107.04517/) (2021)
- [Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty Estimation](https://mlsanity.com/paper/2110.10563/) (2021)
- [EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning](https://mlsanity.com/paper/2303.03037/) (2023)
- [Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network](https://mlsanity.com/paper/2307.12309/) (2023)
- [Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space](https://mlsanity.com/paper/2404.08923/) (2024)
- [Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery](https://mlsanity.com/paper/2207.06673/) (2022)
- [Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibration](https://mlsanity.com/paper/2306.08981/) (2023)
- [BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection](https://mlsanity.com/paper/2403.18373/) (2024)
- [An Uncertainty Estimation Framework for Probabilistic Object Detection](https://mlsanity.com/paper/2106.15007/) (2021)
- [UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking](https://mlsanity.com/paper/2402.12303/) (2024)
- [Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss](https://mlsanity.com/paper/2209.00364/) (2022)
- [MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimation](https://mlsanity.com/paper/2410.08739/) (2024)
- [Uncertainty-Aware Gradient Stabilization for Small Object Detection](https://mlsanity.com/paper/2303.01803/) (2023)
- [Fast and Reliable Probabilistic Face Embeddings in the Wild](https://mlsanity.com/paper/2102.04075/) (2021)
- [Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling](https://mlsanity.com/paper/2108.00784/) (2021)
- [A Trustworthiness Score to Evaluate DNN Predictions](https://mlsanity.com/paper/2301.08839/) (2023)

## Abstract (arXiv)

Environment perception is the task for intelligent vehicles on which all subsequent steps rely. A key part of perception is to safely detect other road users such as vehicles, pedestrians, and cyclists. With modern deep learning techniques huge progress was made over the last years in this field. However such deep learning based object detection models cannot predict how certain they are in their predictions, potentially hampering the performance of later steps such as tracking or sensor fusion. We present a viable approaches to estimate uncertainty in an one-stage object detector, while improving the detection performance of the baseline approach. The proposed model is evaluated on a large scale automotive pedestrian dataset. Experimental results show that the uncertainty outputted by our system is coupled with detection accuracy and the occlusion level of pedestrians.