Multiple Objects Localization Using Image Segmentation with U-Net

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F21%3A39918540" target="_blank" >RIV/00216275:25530/21:39918540 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/PC52310.2021.9447488" target="_blank" >http://dx.doi.org/10.1109/PC52310.2021.9447488</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/PC52310.2021.9447488" target="_blank" >10.1109/PC52310.2021.9447488</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multiple Objects Localization Using Image Segmentation with U-Net

Popis výsledku v původním jazyce

Precise object localization in an industrial environment is a significant task affecting follow-up processes for a pick and place application. One of the solutions to effectively ensure the success of this task is to use modern methods of machine vision. Machine vision is still a highly evolving topic, in which the use of approaches based on convolutional neural networks is rising. And so in this contribution, an innovative engineering approach based on convolutional neural networks is proposed for an object localization task. The approach is based on an atypical image segmentation, where the individual objects are represented by two colored gradient circles. These circles represent significant parts of the object like its center or ending. Each object type (class) is determined by a specific color. By use of a local maxima finder, all circles in an image are transformed to points. With knowledge of these points the coordinates and rotations are calculated. The proposed approach was tested on a legitimate localization problem with 100% precision, more than 99.52% recall on the positioning task and with an average of 6 minutes angle variance per object.

Název v anglickém jazyce

Multiple Objects Localization Using Image Segmentation with U-Net

Popis výsledku anglicky

Precise object localization in an industrial environment is a significant task affecting follow-up processes for a pick and place application. One of the solutions to effectively ensure the success of this task is to use modern methods of machine vision. Machine vision is still a highly evolving topic, in which the use of approaches based on convolutional neural networks is rising. And so in this contribution, an innovative engineering approach based on convolutional neural networks is proposed for an object localization task. The approach is based on an atypical image segmentation, where the individual objects are represented by two colored gradient circles. These circles represent significant parts of the object like its center or ending. Each object type (class) is determined by a specific color. By use of a local maxima finder, all circles in an image are transformed to points. With knowledge of these points the coordinates and rotations are calculated. The proposed approach was tested on a legitimate localization problem with 100% precision, more than 99.52% recall on the positioning task and with an average of 6 minutes angle variance per object.

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)

Projekt

<a href="/cs/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Spolupráce Univerzity Pardubice a aplikační sféry v aplikačně orientovaném výzkumu lokačních, detekčních a simulačních systémů pro dopravní a přepravní procesy (PosiTrans)</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2021 23rd International Conference on Process Control, PC 2021

ISBN

978-1-66540-330-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

180-185

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

ONLINE

Datum konání akce

1. 6. 2021

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000723653400031