Boosting the Performance of Object Detection CNNs with Context-Based Anomaly Detection
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Boosting the Performance of Object Detection CNNs with Context-Based Anomaly Detection
Popis výsledku v původním jazyce
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
Název v anglickém jazyce
Boosting the Performance of Object Detection CNNs with Context-Based Anomaly Detection
Popis výsledku anglicky
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
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
ISBN
978-3-030-67536-3
ISSN
1867-8211
e-ISSN
—
Počet stran výsledku
18
Strana od-do
159-176
Název nakladatele
Springer Nature
Místo vydání
—
Místo konání akce
Shanghai
Datum konání akce
16. 10. 2020
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—