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旋转敏感特征增强网络用于远程传感图像中的定向对象检测.

Jiaxin Xu1, Hua Huo1, Shilu Kang1

  • 1College of Information Engineering and Artificial Intelligence, Henan University of Science and Technology, Luoyang 471000, China.

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概括
此摘要是机器生成的。

这项研究引入了一种增强的旋转敏感特征金字塔网络 (RSFPN),用于精确地在遥感图像中检测定向对象. 通过解决特征表示和优化挑战,RSFPN框架显著提高了性能.

关键词:
注意力机制注意力机制特征金字塔的特征是金字塔的特征.几何一致性的几何一致性远程传感是一种遥感技术.对旋转敏感的旋转灵敏度很高.

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科学领域:

  • 计算机视觉 计算机视觉
  • 遥感 图像分析 图像分析
  • 机器学习 机器学习

背景情况:

  • 由于任意旋转,尺度变化和复杂的背景,远程传感中的定向对象检测具有挑战性.
  • 现有的旋转探测器有着不够的方向敏感特征,特征错位,不稳定的旋转参数优化等问题.

研究的目的:

  • 提出一个增强的旋转敏感特征金字塔网络 (RSFPN),以克服当前旋转物体探测器的局限性.
  • 为了提高远程传感图像中定向物体检测的准确性和效率.

主要方法:

  • 引入了一个动态自适应特征金字塔网络 (DAFPN),用于双向的多尺度特征融合.
  • 开发了一个角度意识协作注意力 (AACA) 模块,使用定向先验来改进功能.
  • 实现了几何一致的多任务损失 (GC-MTL) 以实现统一的旋转参数回归与光滑和自适应权重.

主要成果:

  • 在DOTA-v1.0上达到77.42%的最先进的平均精度 (mAP),在HRSC2016.0上达到91.85%的平均精度.
  • 保持了14.5 FPS的高效推断速度,证明了强大的准确性-效率权衡.
  • 视觉分析证实了集中,旋转意识的特征反应和有效的背景抑制.

结论:

  • 拟议的RSFPN框架为在高分辨率遥感图像中检测多方向物体提供了强大的解决方案.
  • 该方法在城市规划,环境监测和安全等应用中具有显著的实际价值.