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

Antoine D’acremont 1 Ronan Fablet 2 Alexandre Baussard 1 Guillaume Quin 3
1 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 Lab-STICC_IMTA_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 LDG - Laboratoire de Détection et de Géophysique (CEA)
DAM/DIF - DAM Île-de-France : DAM/DIF
Abstract : 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.
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https://hal-ensta-bretagne.archives-ouvertes.fr/hal-02122964
Contributeur : Marie Briec <>
Soumis le : mardi 7 mai 2019 - 16:21:30
Dernière modification le : mercredi 24 juin 2020 - 16:19:52

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Antoine D’acremont, Ronan Fablet, Alexandre Baussard, Guillaume Quin. CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems. Sensors, MDPI, 2019, 19 (9), pp.2040. ⟨10.3390/s19092040⟩. ⟨hal-02122964⟩

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