Automatic target recognition (ATR) is an important part for many computer vision applications. Despite the extensive research which has been carried out in this area for many years, there is no ATR system which performs well on all applications. Recently, different object recognition frameworks have been proposed which yield a high performance in baseline databases. However, our experiments showed that they can fail in real-world scenarios, when dealing with a limited number of data samples. In this paper, we propose a new 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 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, the 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.

中文翻译：

---

DeepTarget：使用深度卷积神经网络的自动目标识别

自动目标识别（ATR）是许多计算机视觉应用的重要组成部分。尽管在该领域进行了多年的广泛研究，但没有一种 ATR 系统在所有应用中都表现良好。最近，已经提出了不同的对象识别框架，它们在基线数据库中产生了高性能。然而，我们的实验表明，在处理有限数量的数据样本时，它们可能会在现实世界中失败。在本文中，我们提出了一种基于深度卷积神经网络 (DCNN) 的新 ATR 系统，用于检测前视红外 (FLIR) 场景中的目标并识别其类别。在我们提出的 ATR 框架中，训练了一个完全卷积的网络以将输入的 FLIR 图像数据映射到固定步幅对应大小的目标分数图。通过在目标评分图上应用阈值来识别潜在目标。最后，以这些目标点为中心的相应区域被馈送到 DCNN，将它们分类为不同的目标类型，同时拒绝误报。与两个大型 FLIR 图像数据库上的最新方法相比，所提出的架构实现了明显更好的性能。