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Proceedings Paper

Classifying objects in LWIR imagery via CNNs
Author(s): Iain Rodger; Barry Connor; Neil M. Robertson
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Paper Abstract

The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave Infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.

Paper Details

Date Published: 21 October 2016
PDF: 14 pages
Proc. SPIE 9987, Electro-Optical and Infrared Systems: Technology and Applications XIII, 99870H (21 October 2016); doi: 10.1117/12.2241858
Show Author Affiliations
Iain Rodger, Heriot Watt Univ. (United Kingdom)
Thales UK (United Kingdom)
Barry Connor, Thales UK (United Kingdom)
Neil M. Robertson, Queen’s Univ. Belfast (United Kingdom)


Published in SPIE Proceedings Vol. 9987:
Electro-Optical and Infrared Systems: Technology and Applications XIII
David A. Huckridge; Reinhard Ebert; Stephen T. Lee, Editor(s)

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