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Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data
During the last few years, supervised deep convolutional neural networks have become the state-of-the-art for image recognition tasks. Nevertheless, their performance is severely linked to the amount and quality of the training data. Acquiring and labeling data is a major challenge that limits their...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967933/ https://www.ncbi.nlm.nih.gov/pubmed/36850495 http://dx.doi.org/10.3390/s23041898 |
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author | Vidal, Joel Vallicrosa, Guillem Martí, Robert Barnada, Marc |
author_facet | Vidal, Joel Vallicrosa, Guillem Martí, Robert Barnada, Marc |
author_sort | Vidal, Joel |
collection | PubMed |
description | During the last few years, supervised deep convolutional neural networks have become the state-of-the-art for image recognition tasks. Nevertheless, their performance is severely linked to the amount and quality of the training data. Acquiring and labeling data is a major challenge that limits their expansion to new applications, especially with limited data. Recognition of Lego bricks is a clear example of a real-world deep learning application that has been limited by the difficulties associated with data gathering and training. In this work, photo-realistic image synthesis and few-shot fine-tuning are proposed to overcome limited data in the context of Lego bricks recognition. Using synthetic images and a limited set of 20 real-world images from a controlled environment, the proposed system is evaluated on controlled and uncontrolled real-world testing datasets. Results show the good performance of the synthetically generated data and how limited data from a controlled domain can be successfully used for the few-shot fine-tuning of the synthetic training without a perceptible narrowing of its domain. Obtained results reach an AP50 value of 91.33% for uncontrolled scenarios and 98.7% for controlled ones. |
format | Online Article Text |
id | pubmed-9967933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99679332023-02-27 Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data Vidal, Joel Vallicrosa, Guillem Martí, Robert Barnada, Marc Sensors (Basel) Article During the last few years, supervised deep convolutional neural networks have become the state-of-the-art for image recognition tasks. Nevertheless, their performance is severely linked to the amount and quality of the training data. Acquiring and labeling data is a major challenge that limits their expansion to new applications, especially with limited data. Recognition of Lego bricks is a clear example of a real-world deep learning application that has been limited by the difficulties associated with data gathering and training. In this work, photo-realistic image synthesis and few-shot fine-tuning are proposed to overcome limited data in the context of Lego bricks recognition. Using synthetic images and a limited set of 20 real-world images from a controlled environment, the proposed system is evaluated on controlled and uncontrolled real-world testing datasets. Results show the good performance of the synthetically generated data and how limited data from a controlled domain can be successfully used for the few-shot fine-tuning of the synthetic training without a perceptible narrowing of its domain. Obtained results reach an AP50 value of 91.33% for uncontrolled scenarios and 98.7% for controlled ones. MDPI 2023-02-08 /pmc/articles/PMC9967933/ /pubmed/36850495 http://dx.doi.org/10.3390/s23041898 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Vidal, Joel Vallicrosa, Guillem Martí, Robert Barnada, Marc Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data |
title | Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data |
title_full | Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data |
title_fullStr | Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data |
title_full_unstemmed | Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data |
title_short | Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data |
title_sort | brickognize: applying photo-realistic image synthesis for lego bricks recognition with limited data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967933/ https://www.ncbi.nlm.nih.gov/pubmed/36850495 http://dx.doi.org/10.3390/s23041898 |
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