Artificially Intelligent Targeting

Date

2020-01-15 15:24

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Cranfield University

Department

Type

Image

ISSN

Format

Free to read from

Citation

Westlake, Samuel (2020). Artificially Intelligent Targeting. Cranfield Online Research Data (CORD). Media. https://doi.org/10.17862/cranfield.rd.11550216.v1

Abstract

The aim of this project is the development of new techniques for infrared anti-ship missile seekers. This image illustrates how we are using deep learning to detect, recognise and classify multiple ships. Our algorithm can differentiate between military and civilian vessels, and is even robust against the presence of infrared countermeasures and background clutter.In most cases, training deep learning algorithms requires thousands, if not millions, of carefully labelled examples. This presents a major challenge for the application of deep learning to infrared missile seekers, as the availability of such training data is extremely limited. To over come this, we simulated multiple thermal signatures for ten different ships and used these to synthetically generate a large and realistic data set. This data was then used to train our artificial neural network, and the subsequent model performed successfully on real-world infrared test data.

Description

Software Description

Software Language

Github

Keywords

'Deep learning', 'Infrared', 'Missile Seekers', 'DSDS19', 'DSDS19 Digital Image', 'Image Processing'

DOI

10.17862/cranfield.rd.11550216.v1

Rights

CC BY-NC-SA 4.0

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Relationships

Supplements

Funder/s

MBDA

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