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.
Nasser M. Nasrabadi, Hadi Kazemi, and Mehdi Iranmanesh, "Automatic target recognition using deep convolutional neural networks," Proc. SPIE 10648, Automatic Target Recognition XXVIII, 106480I (Presented at SPIE Defense + Security: April 17, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2304643.
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