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@inproceedings{2392228,
author = {Hashemi, Vahid and Křetínský, Jan and Rieder, Sabine and Schmidt, Jessica},
address = {Lübeck},
booktitle = {FORMAL METHODS, FM 2023},
doi = {http://dx.doi.org/10.1007/978-3-031-27481-7_36},
editor = {978-3-031-27481-7},
keywords = {Runtime monitoring; Neural networks; Out-of-distribution detection; Object detection},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Lübeck},
isbn = {978-3-031-27480-0},
pages = {622-634},
publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},
title = {Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks},
year = {2023}
}
TY - JOUR
ID - 2392228
AU - Hashemi, Vahid - Křetínský, Jan - Rieder, Sabine - Schmidt, Jessica
PY - 2023
TI - Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks
PB - SPRINGER INTERNATIONAL PUBLISHING AG
CY - Lübeck
SN - 9783031274800
KW - Runtime monitoring
KW - Neural networks
KW - Out-of-distribution detection
KW - Object detection
N2 - Runtime monitoring provides a more realistic and applicable alternative to verification in the setting of real neural networks used in industry. It is particularly useful for detecting out-of-distribution (OOD) inputs, for which the network was not trained and can yield erroneous results. We extend a runtime-monitoring approach previously proposed for classification networks to perception systems capable of identification and localization of multiple objects. Furthermore, we analyze its adequacy experimentally on different kinds of OOD settings, documenting the overall efficacy of our approach.
ER -
HASHEMI, Vahid, Jan KŘETÍNSKÝ, Sabine RIEDER and Jessica SCHMIDT. Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks. In 978-3-031-27481-7. \textit{FORMAL METHODS, FM 2023}. Lübeck: SPRINGER INTERNATIONAL PUBLISHING AG, 2023, p.~622-634. ISBN~978-3-031-27480-0. Available from: https://dx.doi.org/10.1007/978-3-031-27481-7\_{}36.
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