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Synthetic Data for Object Classification in Industrial Applications
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-9696-7843
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-1400-346X
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2023 (English)In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM / [ed] Maria De Marsico; Gabriella Sanniti di Baja; Ana Fred, SciTePress, 2023, Vol. 1, p. 387-394Conference paper, Published paper (Refereed)
Abstract [en]

One of the biggest challenges in machine learning is data collection. Training data is an important part since it determines how the model will behave. In object classification, capturing a large number of images per object and in different conditions is not always possible and can be very time-consuming and tedious. Accordingly, this work explores the creation of artificial images using a game engine to cope with limited data in the training dataset. We combine real and synthetic data to train the object classification engine, a strategy that has shown to be beneficial to increase confidence in the decisions made by the classifier, which is often critical in industrial setups. To combine real and synthetic data, we first train the classifier on a massive amount of synthetic data, and then we fine-tune it on real images. Another important result is that the amount of real images needed for fine-tuning is not very high, reaching top accuracy with just 12 or 24 images per class. This substantially reduces the requirements of capturing a great amount of real data. © 2023 by SCITEPRESS-Science and Technology Publications, Lda.

Place, publisher, year, edition, pages
SciTePress, 2023. Vol. 1, p. 387-394
Keywords [en]
Synthetic Data, Object Classification, Machine Learning, Computer Vision, ResNet50
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-48794DOI: 10.5220/0011689900003411Scopus ID: 2-s2.0-85174507299OAI: oai:DiVA.org:hh-48794DiVA, id: diva2:1717737
Conference
12th International Conference on Pattern Recognition Applications and Methods, ICPRAM, Lisbon, Portugal, February 22-24, 2023
Projects
2021-05038 Vinnova DIFFUSE Disentanglement of Features For Utilization in Systematic Evaluation
Part of project
Facial Analysis in the Era of Mobile Devices and Face Masks, Swedish Research Council
Funder
Swedish Research CouncilVinnovaAvailable from: 2022-12-09 Created: 2022-12-09 Last updated: 2024-06-17Bibliographically approved

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Hernandez-Diaz, KevinAlonso-Fernandez, Fernando

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Baaz, AugustHernandez-Diaz, KevinAlonso-Fernandez, Fernando
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