Detection of Door-Closing Defects by Learning from Physics-Based Simulations

Ryoga Takahashi, Yota Yamamoto, Ryosuke Furuta, Yukinobu Taniguchi

2025

Abstract

In this paper, we propose a method that applies physics-based simulations for detecting door-closing defects. Quantitative inspection of industrial products is essential to reduce human errors and variation in inspection results. Door-closing inspections, which now rely on human sensory evaluation, are prime targets for quantification and automation. Developing a visual inspection model based on deep learning requires time-consuming and labor-intensive data collection with dedicated measuring instruments. To eliminate the need for expensive data collection, our proposal uses physics-based simulation data instead of real data to learn the physical relationships. Specifically, we simultaneously learn a binary classification task for normal and defective doors and a task for estimating door-closing energy while sharing parameters, which allows us to learn the relationships between them in a preliminary step. Experiments demonstrate that our method has greater accuracy than existing methods and achieves an accuracy comparable to the method that uses ground-truth data collected with dedicated measuring instruments.

Paper Citation

in Harvard Style

Takahashi R., Yamamoto Y., Furuta R. and Taniguchi Y. (2025). Detection of Door-Closing Defects by Learning from Physics-Based Simulations. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 93-98. DOI: 10.5220/0013148200003912

in Bibtex Style

@conference{visapp25,
author={Ryoga Takahashi and Yota Yamamoto and Ryosuke Furuta and Yukinobu Taniguchi},
title={Detection of Door-Closing Defects by Learning from Physics-Based Simulations},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={93-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013148200003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Detection of Door-Closing Defects by Learning from Physics-Based Simulations
SN - 978-989-758-728-3
AU - Takahashi R.
AU - Yamamoto Y.
AU - Furuta R.
AU - Taniguchi Y.
PY - 2025
SP - 93
EP - 98
DO - 10.5220/0013148200003912
PB - SciTePress