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Open AccessArticle

CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems

1
ENSTA-Bretagne, UMR 6285 labSTICC, 29806 Brest, France
2
MBDA France, 92350 Le Plessis-Robinson, France
3
Institut Mines-Télécom, UMR 6285 labSTICC, 29238 Brest, France
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2040; https://doi.org/10.3390/s19092040
Received: 28 February 2019 / Revised: 18 April 2019 / Accepted: 26 April 2019 / Published: 30 April 2019
(This article belongs to the Special Issue Deep Learning Remote Sensing Data)
Convolutional neural networks (CNNs) have rapidly become the state-of-the-art models for image classification applications. They usually require large groundtruthed datasets for training. Here, we address object identification and recognition in the wild for infrared (IR) imaging in defense applications, where no such large-scale dataset is available. With a focus on robustness issues, especially viewpoint invariance, we introduce a compact and fully convolutional CNN architecture with global average pooling. We show that this model trained from realistic simulation datasets reaches a state-of-the-art performance compared with other CNNs with no data augmentation and fine-tuning steps. We also demonstrate a significant improvement in the robustness to viewpoint changes with respect to an operational support vector machine (SVM)-based scheme. View Full-Text
Keywords: deep learning; CNN; target identification and recognition; infrared imaging deep learning; CNN; target identification and recognition; infrared imaging
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MDPI and ACS Style

d’Acremont, A.; Fablet, R.; Baussard, A.; Quin, G. CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems. Sensors 2019, 19, 2040.

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