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Article Dans Une Revue Sensors Année : 2019

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

Résumé

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.

Dates et versions

hal-02122964 , version 1 (07-05-2019)

Identifiants

Citer

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