Automated Object Labeling For CNN-Based Image Segmentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00533825" target="_blank" >RIV/67985556:_____/20:00533825 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICIP40778.2020.9191320" target="_blank" >http://dx.doi.org/10.1109/ICIP40778.2020.9191320</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICIP40778.2020.9191320" target="_blank" >10.1109/ICIP40778.2020.9191320</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated Object Labeling For CNN-Based Image Segmentation

Popis výsledku v původním jazyce

Deep learning-based methods for classification and segmentation require large training sets. Generating training data is often a tedious and expensive task. In industrial applications, such as automated visual inspection of products in an assemble line, objects for classification are well defined yet labeled data are difficult to obtain. To alleviate the problem of manual labeling, we propose to train a convolutional neural network with an automatically generated training set using a naive classifier with handcrafted features. We show that when the naive classifier has high precision then the trained network has both high precision and recall despite the low recall of the naive classifier. We demonstrate the proposed methodology on real scenario of detecting a car coolant tank. However, the proposed methodology facilitates collection of train data for a wider type of CNN based methods such as near-duplicate image detection or segmenting tampered areas of images.

Název v anglickém jazyce

Automated Object Labeling For CNN-Based Image Segmentation

Popis výsledku anglicky

Deep learning-based methods for classification and segmentation require large training sets. Generating training data is often a tedious and expensive task. In industrial applications, such as automated visual inspection of products in an assemble line, objects for classification are well defined yet labeled data are difficult to obtain. To alleviate the problem of manual labeling, we propose to train a convolutional neural network with an automatically generated training set using a naive classifier with handcrafted features. We show that when the naive classifier has high precision then the trained network has both high precision and recall despite the low recall of the naive classifier. We demonstrate the proposed methodology on real scenario of detecting a car coolant tank. However, the proposed methodology facilitates collection of train data for a wider type of CNN based methods such as near-duplicate image detection or segmenting tampered areas of images.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20206 - Computer hardware and architecture

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)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

2020 IEEE International Conference on Image Processing (ICIP)

ISBN

978-1-7281-6396-3

ISSN

1522-4880

e-ISSN

2381-8549

Počet stran výsledku

5

Strana od-do

2036-2040

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Abu Dhabi

Datum konání akce

25. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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