Boosting the Performance of Object Detection CNNs with Context-Based Anomaly Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351755" target="_blank" >RIV/68407700:21230/21:00351755 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-030-67537-0_11" target="_blank" >https://doi.org/10.1007/978-3-030-67537-0_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-67537-0_11" target="_blank" >10.1007/978-3-030-67537-0_11</a>

Result language

angličtina

Original language name

Boosting the Performance of Object Detection CNNs with Context-Based Anomaly Detection

Original language description

In this paper, we employ anomaly detection methods to enhance the ability of object detectors by using the context of their detections. This has numerous potential applications from boosting the performance of standard object detectors, to the preliminary validation of annotation quality, and even for robotic exploration and object search. We build our method on autoencoder networks for detecting anomalies, where we do not try to filter incoming data based on anomality score as is usual, but instead, we focus on the individual features of the data representing an actual scene. We show that one can teach autoencoders about the contextual relationship of objects in images, i.e. the likelihood of co-detecting classes in the same scene. This can then be used to identify detections that do and do not fit with the rest of the current observations in the scene. We show that the use of this information yields better results than using traditional thresholding when deciding if weaker detections are actually classed as observed or not. The experiments performed not only show that our method significantly improves the performance of CNN object detectors, but that it can be used as an efficient tool to discover incorrectly-annotated images

Czech name

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Czech description

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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)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

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

Article name in the collection

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering

ISBN

978-3-030-67536-3

ISSN

1867-8211

e-ISSN

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Number of pages

18

Pages from-to

159-176

Publisher name

Springer Nature

Place of publication

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Event location

Shanghai

Event date

Oct 16, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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