• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >DeepTarget: An Automatic Target Recognition Using Deep Convolutional Neural Networks
【24h】

DeepTarget: An Automatic Target Recognition Using Deep Convolutional Neural Networks

机译：Deeptarget：使用深卷积神经网络的自动目标识别

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：自动目标识别（ATR）是许多计算机视觉应用程序的重要组成部分。尽管在该地区进行了广泛的研究多年来，但没有ATR系统在所有应用中表现良好。最近，已经提出了不同的对象识别框架，从而在基线数据库中产生高性能。然而，我们的实验表明，在处理有限数量的数据样本时，他们可以在现实世界中失败。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，以检测前瞻性红外线（FLIR）场景的目标并识别其课程。在我们提出的ATR框架中，培训完全卷积的网络，以将输入FLIR图像数据映射到一个固定的跨度相应的目标分数图。通过在目标得分图上应用阈值来识别潜在目标。最后，在这些目标点处居中的相应区域被馈送到DCNN以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图像数据库上的最先进方法相比，该建筑的拟议体系结构实现了显着更好的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号