Synthetic Data for Object Classification in Industrial Applications

August Baaz, Yonan Yonan, Kevin Hernandez-Diaz, Fernando Alonso-Fernandez, Felix Nilsson

2023

Abstract

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.

Paper Citation

in Harvard Style

Baaz A., Yonan Y., Hernandez-Diaz K., Alonso-Fernandez F. and Nilsson F. (2023). Synthetic Data for Object Classification in Industrial Applications. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 387-394. DOI: 10.5220/0011689900003411

in Bibtex Style

@conference{icpram23,
author={August Baaz and Yonan Yonan and Kevin Hernandez-Diaz and Fernando Alonso-Fernandez and Felix Nilsson},
title={Synthetic Data for Object Classification in Industrial Applications},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={387-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011689900003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Synthetic Data for Object Classification in Industrial Applications
SN - 978-989-758-626-2
AU - Baaz A.
AU - Yonan Y.
AU - Hernandez-Diaz K.
AU - Alonso-Fernandez F.
AU - Nilsson F.
PY - 2023
SP - 387
EP - 394
DO - 10.5220/0011689900003411