Citation: Man, K.; Chahl, J. A Review
of Synthetic Image Data and Its Use
in Computer Vision. J. Imaging 2022,
8, 310. https://doi.org/10.3390/
jimaging8110310
Academic Editors: Silvia Liberata
Ullo and Cosimo Distante
Received: 19 October 2022
Accepted: 15 November 2022
Published: 21 November 2022
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Review
A Review of Synthetic Image Data and Its Use in
Computer Vision
Keith Man * and Javaan Chahl
UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
* Correspondence: keith.man@mymail.unisa.edu.au; Tel.: +61-466-993-434
Abstract:
Development of computer vision algorithms using convolutional neural networks and
deep learning has necessitated ever greater amounts of annotated and labelled data to produce high
performance models. Large, public data sets have been instrumental in pushing forward computer
vision by providing the data necessary for training. However, many computer vision applications
cannot rely on general image data provided in the available public datasets to train models, instead
requiring labelled image data that is not readily available in the public domain on a large scale. At
the same time, acquiring such data from the real world can be difficult, costly to obtain, and manual
labour intensive to label in large quantities. Because of this, synthetic image data has been pushed to
the forefront as a potentially faster and cheaper alternative to collecting and annotating real data. This
review provides general overview of types of synthetic image data, as categorised by synthesised
output, common methods of synthesising different types of image data, existing applications and
logical extensions, performance of synthetic image data in different applications and the associated
difficulties in assessing data performance, and areas for further research.
Keywords: computer vision; image synthesis; synthetic image data; synthetic data generation
1. Introduction
Modern approaches to computer vision primarily center around the use convolutional
neural networks (CNN) and deep learning networks to train image processing models,
methods which necessitate large amounts of labelled data and significant computational
resources for training, while it is possible to use unlabelled data via unsupervised learning
to train some computer vision models, the resulting performance is typically inferior to
training via supervised learning and, in some applications, can fail to produce a model
with meaningful performance [
1
]. The need for large quantities of labelled data makes
it difficult for computer vision to be utilised in applications where the collection of large
amounts of data is impractical, labelling data is costly, or a combination of both. Medical
applications struggle with large scale data collection, crowd counting annotation remains
a labour intensive task when done manually, and niche applications such as vital sign
detection from drones suffers from both. Harder still is ensuring that the collected data is of
sufficient quality and diversity to train a robust computer vision model to the performance
level required by the application. These difficulties in image data acquisition has seen an
increase in interest in synthetic image data as a potentially cheaper and more accessible
alternative to acquiring real data for training, while multiple data types used in the field of
computer vision, this review paper is primarily focused on evaluating the use of camera
like image data and methods of generating such data synthetically. As such, the synthetic
generation of data types that have seen use in computer vision, such as radar scans, sonar
scans, and lidar point clouds are not are not considered.
Synthetic image data is defined in this review as any image data that is either artificially
created by modifying real image data or captured from synthetic environments. This can
take many forms, including the digital manipulation of real data [
2
] and the capture of
J. Imaging 2022, 8, 310. https://doi.org/10.3390/jimaging8110310 https://www.mdpi.com/journal/jimaging
