Automatic target recognition using deep convolutional neural networks

Nasser M. Nasrabadi; Hadi Kazemi; Mehdi Iranmanesh

doi:10.1117/12.2304643

30 April 2018 Automatic target recognition using deep convolutional neural networks

Nasser M. Nasrabadi, Hadi Kazemi, Mehdi Iranmanesh

Proceedings Volume 10648, Automatic Target Recognition XXVIII; 106480I (2018) https://doi.org/10.1117/12.2304643
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

In this paper, we propose a new Automatic Target Recognition (ATR) system, based on Deep Convolutional Neural Network (DCNN), to detect the targets in Forward Looking Infrared (FLIR) scenes and recognize their classes. In our proposed ATR framework, a fully convolutional network (FCN) is trained to map the input FLIR imagery data to a fixed stride correspondingly-sized target score map. The potential targets are identified by applying a threshold on the target score map. Finally, corresponding regions centered at these target points are fed to a DCNN to classify them into different target types while at the same time rejecting the false alarms. The proposed architecture achieves a significantly better performance in comparison with that of the state-of-the-art methods on two large FLIR image databases.

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Citation Download Citation

Nasser M. Nasrabadi, Hadi Kazemi, and Mehdi Iranmanesh "Automatic target recognition using deep convolutional neural networks", Proc. SPIE 10648, Automatic Target Recognition XXVIII, 106480I (30 April 2018); https://doi.org/10.1117/12.2304643

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