# BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

arXiv: https://arxiv.org/abs/1903.03838 · HTML: https://mlsanity.com/paper/1903.03838/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2019.

## Built on by (most cited first, 30 of 68)

- [DDP: Diffusion Model for Dense Visual Prediction](https://mlsanity.com/paper/2303.17559/) (2023)
- [Active Learning for Deep Object Detection via Probabilistic Modeling](https://mlsanity.com/paper/2103.16130/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors](https://mlsanity.com/paper/2101.05036/) (2021)
- [OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving](https://mlsanity.com/paper/2404.15014/) (2024)
- [Uncertainty for Identifying Open-Set Errors in Visual Object Detection](https://mlsanity.com/paper/2104.01328/) (2021)
- [Exploring Active 3D Object Detection from a Generalization Perspective](https://mlsanity.com/paper/2301.09249/) (2023)
- [GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation](https://mlsanity.com/paper/2207.02466/) (2022)
- [Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction](https://mlsanity.com/paper/2403.07263/) (2024)
- [Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling](https://mlsanity.com/paper/2304.06052/) (2023)
- [Monte Carlo DropBlock for Modelling Uncertainty in Object Detection](https://mlsanity.com/paper/2108.03614/) (2021)
- [Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation](https://mlsanity.com/paper/2205.11047/) (2022)
- [Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining](https://mlsanity.com/paper/2210.08375/) (2022)
- [Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration](https://mlsanity.com/paper/2307.00934/) (2023)
- [RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses](https://mlsanity.com/paper/2403.01795/) (2024)
- [I Find Your Lack of Uncertainty in Computer Vision Disturbing](https://mlsanity.com/paper/2104.08188/) (2021)
- [Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors](https://mlsanity.com/paper/2107.04517/) (2021)
- [Semantic Segmentation with Active Semi-Supervised Learning](https://mlsanity.com/paper/2203.10730/) (2022)
- [CertainNet: Sampling-free Uncertainty Estimation for Object Detection](https://mlsanity.com/paper/2110.01604/) (2021)
- [Bayesian Deep Learning for Affordance Segmentation in images](https://mlsanity.com/paper/2303.00871/) (2023)
- [KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection](https://mlsanity.com/paper/2307.07942/) (2023)
- [Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes](https://mlsanity.com/paper/2109.09690/) (2021)
- [Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty Estimation](https://mlsanity.com/paper/2110.10563/) (2021)
- [Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications](https://mlsanity.com/paper/2309.13207/) (2023)
- [UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network](https://mlsanity.com/paper/2503.12888/) (2025)
- [A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction](https://mlsanity.com/paper/2110.03446/) (2021)
- [EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning](https://mlsanity.com/paper/2303.03037/) (2023)
- [Parametric and Multivariate Uncertainty Calibration for Regression and Object Detection](https://mlsanity.com/paper/2207.01242/) (2022)
- [Bayesian Active Learning for Sim-to-Real Robotic Perception](https://mlsanity.com/paper/2109.11547/) (2021)
- [Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space](https://mlsanity.com/paper/2404.08923/) (2024)

## Abstract (arXiv)

When incorporating deep neural networks into robotic systems, a major challenge is the lack of uncertainty measures associated with their output predictions. Methods for uncertainty estimation in the output of deep object detectors (DNNs) have been proposed in recent works, but have had limited success due to 1) information loss at the detectors non-maximum suppression (NMS) stage, and 2) failure to take into account the multitask, many-to-one nature of anchor-based object detection. To that end, we introduce BayesOD, an uncertainty estimation approach that reformulates the standard object detector inference and Non-Maximum suppression components from a Bayesian perspective. Experiments performed on four common object detection datasets show that BayesOD provides uncertainty estimates that are better correlated with the accuracy of detections, manifesting as a significant reduction of 9.77%-13.13% on the minimum Gaussian uncertainty error metric and a reduction of 1.63%-5.23% on the minimum Categorical uncertainty error metric. Code will be released at urlhttps://github.com/asharakeh/bayes-od-rc.