Training Convolutional Neural Networks with Synthesized Data for Object Recognition in Industrial Manufacturing

Jason Li, Per Lage Götvall, Julien Provost, Knut Åkesson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Visual tasks such as automated quality control or packaging require machines to be able to detect and identify objects automatically. In recent years object detection systems using deep learning have made significant advancements achieving better scores at a higher performance. However, these methods typically require large amounts of annotated images for training, which are costly and labor intensive to create. Therefore, it is an attractive alternative to generate the training data synthetically using computer-generated imagery (CGI). In this paper, we investigate how to add realistic texture to CAD objects to generate synthetic data for training of an instance segmentation network (Mask R-CNN) for recognition of manufacturing components. The results show that it is possible to create synthetic data with negligible human effort when using simple procedural materials.

Original languageEnglish
Title of host publicationProceedings - 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1544-1547
Number of pages4
ISBN (Electronic)9781728103037
DOIs
StatePublished - Sep 2019
Event24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019 - Zaragoza, Spain
Duration: 10 Sep 201913 Sep 2019

Publication series

NameIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume2019-September
ISSN (Print)1946-0740
ISSN (Electronic)1946-0759

Conference

Conference24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019
Country/TerritorySpain
CityZaragoza
Period10/09/1913/09/19

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