Road Anomaly Detection by Partial Image Reconstruction with Segmentation Coupling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354693" target="_blank" >RIV/68407700:21230/21:00354693 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICCV48922.2021.01536" target="_blank" >https://doi.org/10.1109/ICCV48922.2021.01536</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCV48922.2021.01536" target="_blank" >10.1109/ICCV48922.2021.01536</a>
Alternative languages
Result language
angličtina
Original language name
Road Anomaly Detection by Partial Image Reconstruction with Segmentation Coupling
Original language description
We present a novel approach to the detection of unknownobjects in the context of autonomous driving. The problemis formulated as anomaly detection, since we assume thatthe unknown stuff or object appearance cannot be learned.To that end, we propose a reconstruction module that can beused with many existing semantic segmentation networks,and that is trained to recognize and reconstruct road (driv-able) surface from a small bottleneck. We postulate thatpoor reconstruction of the road surface is due to areas thatare outside of the training distribution, which is a strong in-dicator of an anomaly. The road structural similarity erroris coupled with the semantic segmentation to incorporateinformation from known classes and produce final per-pixelanomaly scores. The proposed JSR-Net was evaluated onfour datasets, Lost-and-found, Road Anomaly, Road Obsta-cles, and FishyScapes, achieving state-of-art performanceon all, reducing the false positives significantly, while typ-ically having the highest average precision for wide rangeof operation points.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICCV2021: Proceedings of the International Conference on Computer Vision
ISBN
978-1-6654-2812-5
ISSN
1550-5499
e-ISSN
2380-7504
Number of pages
10
Pages from-to
15651-15660
Publisher name
IEEE
Place of publication
Piscataway
Event location
Montreal
Event date
Oct 11, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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