• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Object Detection with Head Direction in Remote Sensing Images Based on Rotational Region CNN
【24h】

Object Detection with Head Direction in Remote Sensing Images Based on Rotational Region CNN

机译：基于旋转区域CNN的遥感影像头部方向目标检测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Object detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. In this paper, we propose a novel detection framework based on rotational region convolution neural network to cope with the problem of non-maximum suppression in dense objects detection. The bounding boxes obtained by adopting our method is the minimum bounding rectangle of object with less redundant regions. Furthermore, we find the head direction of the object through prediction. There are three important changes to our framework over traditional detection methods, representation and regression of rotational bounding box, head direction prediction and rotational non-maximal suppression. Experiments based on remote sensing images from Google Earth for Object detection show that our detection method based on rotational region CNN has a competitive performance.
机译：物体检测在遥感领域中一直发挥着重要作用，但仍然充满挑战。在本文中，我们提出了一种基于旋转区域卷积神经网络的新型检测框架，以解决密集物体检测中的非最大抑制问题。通过我们的方法获得的边界框是具有较少冗余区域的对象的最小边界矩形。此外，我们通过预测找到对象的头部方向。与传统的检测方法相比，我们的框架发生了三个重要变化，即旋转边界框的表示和回归，磁头方向预测和旋转非最大抑制。基于来自Google Earth的遥感图像进行目标检测的实验表明，我们基于旋转区域CNN的检测方法具有竞争优势。

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