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Download fileInvestigating the optimisation of real-world and synthetic object detection training datasets through the consideration of environmental and simulation factors
journal contribution
posted on 2022-04-28, 08:14 authored by Callum NewmanCallum Newman, Jon PetzingJon Petzing, Yee GohYee Goh, Laura JusthamLaura JusthamComputer vision is used for many industrial applications involving automation, especially those related to efficiency and safety. Computer vision techniques which use machine learning, such as object detectors, need a dataset of images for training and testing. Publicly available datasets or new dataset can be used. However, these datasets rarely consider whether the dataset is leading to optimal performance. Environmental factors, such as lighting and occlusion, will alter the appearance of the images and so images taken under certain condition may have different effects on training. A knowledge gap has formed as to how the test performance of deep neural networks can be improved by considering the effect and interactions of factors where either real or synthetic images are used. The following research illustrates that the different factors can have a significant impact on the test performance and demonstrates a process that can be used on real-world and synthetic images to identify the effect of each factor and discusses how this information may be used to create an optimal training dataset.
Funding
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
Intelligent Systems with ApplicationsVolume
14Publisher
ElsevierVersion
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher statement
This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/Acceptance date
2022-04-11Publication date
2022-04-12Copyright date
2022ISSN
2667-3053Publisher version
Language
- en