Multiple Objects Localization Using Image Segmentation with U-Net
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Multiple Objects Localization Using Image Segmentation with U-Net
Original language description
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.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
<a href="/en/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2021 23rd International Conference on Process Control, PC 2021
ISBN
978-1-66540-330-6
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
180-185
Publisher name
IEEE (Institute of Electrical and Electronics Engineers)
Place of publication
New York
Event location
ONLINE
Event date
Jun 1, 2021
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000723653400031