CertainNet: Sampling-free Uncertainty Estimation for Object Detection
- URL: http://arxiv.org/abs/2110.01604v1
- Date: Mon, 4 Oct 2021 17:59:31 GMT
- Title: CertainNet: Sampling-free Uncertainty Estimation for Object Detection
- Authors: Stefano Gasperini, Jan Haug, Mohammad-Ali Nikouei Mahani, Alvaro
Marcos-Ramiro, Nassir Navab, Benjamin Busam, Federico Tombari
- Abstract summary: Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings.
In this work, we propose a novel sampling-free uncertainty estimation method for object detection.
We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
- Score: 65.28989536741658
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Estimating the uncertainty of a neural network plays a fundamental role in
safety-critical settings. In perception for autonomous driving, measuring the
uncertainty means providing additional calibrated information to downstream
tasks, such as path planning, that can use it towards safe navigation. In this
work, we propose a novel sampling-free uncertainty estimation method for object
detection. We call it CertainNet, and it is the first to provide separate
uncertainties for each output signal: objectness, class, location and size. To
achieve this, we propose an uncertainty-aware heatmap, and exploit the
neighboring bounding boxes provided by the detector at inference time. We
evaluate the detection performance and the quality of the different uncertainty
estimates separately, also with challenging out-of-domain samples: BDD100K and
nuImages with models trained on KITTI. Additionally, we propose a new metric to
evaluate location and size uncertainties. When transferring to unseen datasets,
CertainNet generalizes substantially better than previous methods and an
ensemble, while being real-time and providing high quality and comprehensive
uncertainty estimates.
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