loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Igor Garcia Ballhausen Sampaio 1 ; Luigy Machaca 1 ; José Viterbo 1 and Joris Guérin 2

Affiliations: 1 Computing Institute, Universidade Federal Fluminense, Brazil ; 2 LAAS-CNRS, ONERA, Université de Toulouse, France

Keyword(s): Object Detection, CAD Models, Synthetic Image Generation, Deep Learning, Convolutional Neural Network.

Abstract: Object Detection (OD) is an important computer vision problem for industry, which can be used for quality control in the production lines, among other applications. Recently, Deep Learning (DL) methods have enabled practitioners to train OD models performing well on complex real world images. However, the adoption of these models in industry is still limited by the difficulty and the significant cost of collecting high quality training datasets. On the other hand, when applying OD to the context of production lines, CAD models of the objects to be detected are often available. In this paper, we introduce a fully automated method that uses a CAD model of an object and returns a fully trained OD model for detecting this object. To do this, we created a Blender script that generates realistic labeled datasets of images containing the object, which are then used for training the OD model. The method is validated experimentally on two practical examples, showing that this approach can gen erate OD models performing well on real images, while being trained only on synthetic images. The proposed method has potential to facilitate the adoption of object detection models in industry as it is easy to adapt for new objects and highly flexible. Hence, it can result in significant costs reduction, gains in productivity and improved products quality. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 52.91.67.23

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Sampaio, I.; Machaca, L.; Viterbo, J. and Guérin, J. (2021). A Novel Method for Object Detection using Deep Learning and CAD Models. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 75-82. DOI: 10.5220/0010451100750082

@conference{iceis21,
author={Igor Garcia Ballhausen Sampaio. and Luigy Machaca. and José Viterbo. and Joris Guérin.},
title={A Novel Method for Object Detection using Deep Learning and CAD Models},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={75-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010451100750082},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Novel Method for Object Detection using Deep Learning and CAD Models
SN - 978-989-758-509-8
IS - 2184-4992
AU - Sampaio, I.
AU - Machaca, L.
AU - Viterbo, J.
AU - Guérin, J.
PY - 2021
SP - 75
EP - 82
DO - 10.5220/0010451100750082
PB - SciTePress