loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Steven Puttemans ; Timothy Callemein and Toon Goedemé

Affiliation: KU Leuven, Belgium

Keyword(s): Deep Learning, Object Detection, Industrial Specific Solutions.

Abstract: The uprising trend of deep learning in computer vision and artificial intelligence can simply not be ignored. On the most diverse tasks, from recognition and detection to segmentation, deep learning is able to obtain state-of-the-art results, reaching top notch performance. In this paper we explore how deep convolutional neural networks dedicated to the task of object detection can improve our industrial-oriented object detection pipelines, using state-of-the-art open source deep learning frameworks, like Darknet. By using a deep learning architecture that integrates region proposals, classification and probability estimation in a single run, we aim at obtaining real-time performance. We focus on reducing the needed amount of training data drastically by exploring transfer learning, while still maintaining a high average precision. Furthermore we apply these algorithms to two industrially relevant applications, one being the detection of promotion boards in eye tracking data and the other detecting and recognizing packages of warehouse products for augmented advertisements. (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 3.145.163.138

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:
Puttemans, S.; Callemein, T. and Goedemé, T. (2018). Building Robust Industrial Applicable Object Detection Models using Transfer Learning and Single Pass Deep Learning Architectures. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 209-217. DOI: 10.5220/0006562002090217

@conference{visapp18,
author={Steven Puttemans. and Timothy Callemein. and Toon Goedemé.},
title={Building Robust Industrial Applicable Object Detection Models using Transfer Learning and Single Pass Deep Learning Architectures},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={209-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006562002090217},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - Building Robust Industrial Applicable Object Detection Models using Transfer Learning and Single Pass Deep Learning Architectures
SN - 978-989-758-290-5
IS - 2184-4321
AU - Puttemans, S.
AU - Callemein, T.
AU - Goedemé, T.
PY - 2018
SP - 209
EP - 217
DO - 10.5220/0006562002090217
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic