Deep learning for automatic target recognition with real and synthetic infrared maritime imagery

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dc.contributor.authorWestlake, Samuel T.
dc.contributor.authorVolonakis, Timothy N.
dc.contributor.authorJackman, James
dc.contributor.authorJames, David B.
dc.contributor.authorSherriff, Andy
dc.date.accessioned2021-05-07T10:22:43Z
dc.date.available2021-05-07T10:22:43Z
dc.date.issued2020-09-20
dc.description.abstractSupervised deep learning algorithms are re-defining the state-of-the-art for object detection and classification. However, training these algorithms requires extensive datasets that are typically expensive and time-consuming to collect. In the field of defence and security, this can become impractical when data is of a sensitive nature, such as infrared imagery of military vessels. Consequently, algorithm development and training are often conducted in synthetic environments, but this brings into question the generalisability of the solution to real world data. In this paper we investigate training deep learning algorithms for infrared automatic target recognition without using real-world infrared data. A large synthetic dataset of infrared images of maritime vessels in the long wave infrared waveband was generated using target-missile engagement simulation software and ten high-fidelity computer-aided design models. Multiple approaches to training a YOLOv3 architecture were explored and subsequently evaluated using a video sequence of real-world infrared data. Experiments demonstrated that supplementing the training data with a small sample of semi-labelled pseudo-IR imagery caused a marked improvement in performance. Despite the absence of real infrared training data, high average precision and recall scores of 99% and 93% respectively were achieved on our real-world test data. To further the development and benchmarking of automatic target recognition algorithms this paper also contributes our dataset of photo-realistic synthetic infrared images.en_UK
dc.identifier.citationWestlake ST, Volonakis TN, Jackman J, et al., (2020) Deep learning for automatic target recognition with real and synthetic infrared maritime imagery. Proceedings of SPIE, Volume 11543, September 2020, Article number 1154309en_UK
dc.identifier.issn0277-786X
dc.identifier.urihttps://doi.org/10.1117/12.2573774
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16654
dc.language.isoenen_UK
dc.publisherSPIEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectdataseten_UK
dc.subjectmaritimeen_UK
dc.subjectsyntheticen_UK
dc.subjectanti-shipen_UK
dc.subjectinfrareden_UK
dc.subjectdeep learningen_UK
dc.subjectAutomatic target recognitionen_UK
dc.titleDeep learning for automatic target recognition with real and synthetic infrared maritime imageryen_UK
dc.typeArticleen_UK

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