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Abstract
LEGO bricks are highly popular due to the ability to build almost any type of creation. This is possible thanks to availability of multiple shapes and colors of the bricks. For the smooth build process the bricks need to properly sorted and arranged. In our work we aim at creating an automated LEGO bricks sorter. With over 3700 different LEGO parts bricks classification has to be done with deep neural networks. The question arises which model of the available should we use? In this paper we try to answer this question. The paper presents a comparison of 28 models used for image classification trained to classify objects to high number of classes with potentially high level of similarity. For that purpose a dataset consisting of 447 classes was prepared. The paper presents brief description of analyzed models, the training and comparison process and discusses the results obtained. Finally the paper proposes an answer what network architecture should be used for the problem of LEGO bricks classification and other similar problems.
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- Accepted or Published Version
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- Copyright (2022 The Author(s), under exclusive license to Springer Nature Switzerland AG)
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Published in:
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LECTURE NOTES IN COMPUTER SCIENCE
no. 13352,
pages 627 - 640,
ISSN: 0302-9743 - Title of issue:
- Computational Science – ICCS 2022 strony 627 - 640
- Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Boiński T., Zawora K., Szymański J.: How to Sort Them? A Network for LEGO Bricks Classification// Computational Science – ICCS 2022/ : , 2022, s.627-640
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-031-08757-8_52
- Sources of funding:
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- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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