CAD-based Learning for Egocentric Object Detection in Industrial Context

Julia Cohen, Julia Cohen, Carlos Crispim-Junior, Céline Grange-Faivre, Laure Tougne

2020

Abstract

Industries nowadays have an increasing need of real-time and accurate vision-based algorithms. Although the performance of object detection methods improved a lot thanks to massive public datasets, instance detection in industrial context must be approached differently, since annotated images are usually unavailable or rare. In addition, when the video stream comes from a head-mounted camera, we observe a lot of movements and blurred frames altering the image content. For this purpose, we propose a framework to generate a dataset of egocentric synthetic images using only CAD models of the objects of interest. To evaluate different strategies exploiting synthetic and real images, we train a Convolutional Neural Network (CNN) for the task of object detection in egocentric images. Results show that training a CNN on synthetic images that reproduce the characteristics of egocentric vision may perform as well as training on a set of real images, reducing, if not removing, the need to manually annotate a large quantity of images to achieve an accurate performance.

Paper Citation

in Harvard Style

Cohen J., Crispim-Junior C., Grange-Faivre C. and Tougne L. (2020). CAD-based Learning for Egocentric Object Detection in Industrial Context. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 644-651. DOI: 10.5220/0008975506440651

in Bibtex Style

@conference{visapp20,
author={Julia Cohen and Carlos Crispim-Junior and Céline Grange-Faivre and Laure Tougne},
title={CAD-based Learning for Egocentric Object Detection in Industrial Context},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={644-651},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008975506440651},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - CAD-based Learning for Egocentric Object Detection in Industrial Context
SN - 978-989-758-402-2
AU - Cohen J.
AU - Crispim-Junior C.
AU - Grange-Faivre C.
AU - Tougne L.
PY - 2020
SP - 644
EP - 651
DO - 10.5220/0008975506440651
PB - SciTePress