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Document type:
Zeitschriftenaufsatz 
Author(s):
Stefano Gasperini; Jan Haug; Mohammad-Ali Nikouei Mahani; Alvaro Marcos-Ramiro; Nassir Navab; Benjamin Busam; Federico Tombari 
Title:
CertainNet: Sampling-free Uncertainty Estimation for Object Detection 
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:...    »
 
Keywords:
uncertainty estimation; object detection; autonomous driving; deep learning; machine learning 
Dewey Decimal Classification:
000 Informatik, Wissen, Systeme 
Journal title:
IEEE Robotics and Automation Letters (RA-L) 
Year:
2021 
Month:
Nov 
Reviewed:
ja 
Language:
en 
Notes:
The first two authors contributed equally. 
Status:
Verlagsversion / published