Image-Consistent Detection of Road Anomalies As Unpredictable Patches

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00362931" target="_blank" >RIV/68407700:21230/23:00362931 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/WACV56688.2023.00545" target="_blank" >https://doi.org/10.1109/WACV56688.2023.00545</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WACV56688.2023.00545" target="_blank" >10.1109/WACV56688.2023.00545</a>

Result language

angličtina

Original language name

Image-Consistent Detection of Road Anomalies As Unpredictable Patches

Original language description

We propose a novel method for anomaly detection primarily aiming at autonomous driving. The design of the method, called DaCUP (Detection of anomalies as Consistent Unpredictable Patches), is based on two general properties of anomalous objects: an anomaly is (i) not from a class that could be modelled and (ii) it is not similar (in appearance) to non-anomalous objects in the image. To this end, we propose a novel embedding bottleneck in an auto-encoder like architecture that enables modelling of a diverse, multi-modal known class appearance (e.g. road). Secondly, we introduce novel image-conditioned distance features that allow known class identification in a nearest-neighbour manner on-the-fly, greatly increasing its ability to distinguish true and false positives. Lastly, an inpainting module is utilized to model the uniqueness of detected anomalies and significantly reduce false positives by filtering regions that are similar, thus reconstructable from their neighbourhood. We demonstrate that filtering of regions based on their similarity to neighbour regions, using e.g. an inpainting module, is general and can be used with other methods for reduction of false positives. The proposed method is evaluated on several publicly available datasets for road anomaly detection and on a maritime benchmark for obstacle avoidance. The method achieves state-of-the-art performance in both tasks with the same hyper-parameters with no domain specific design.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proc. of the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

ISBN

978-1-6654-9346-8

ISSN

2472-6737

e-ISSN

2642-9381

Number of pages

10

Pages from-to

5480-5489

Publisher name

IEEE

Place of publication

Piscataway

Event location

Waikoloa

Event date

Jan 3, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000971500205058