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MSRS-DETR:用于多尺度遥感的端到端对象检测.

Jie Yuan1,2, Shuyi Feng1,2, Hao Han1

  • 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210024, China.

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概括

这项研究介绍了MSRS-DETR,这是一个用于遥感图像物体检测的新框架. 它有效地融合了空间和频率信息,提高了准确性和效率,特别是对于小物体.

关键词:
这就是DETR.频率域分析频率域分析频率 空间融合 频率多个尺度检测检测多个尺度检测.对象检测检测对象检测对象检测远程传感是一种遥感技术.小物体检测 小物体检测

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科学领域:

  • 计算机视觉 计算机视觉
  • 遥感 遥感 遥感 遥感
  • 机器学习 机器学习

背景情况:

  • 遥感图像 (RSI) 中的物体检测对于各种应用至关重要.
  • 目前的探测器与光谱偏差作斗争,限制高频信息学习,这导致混乱的背景,分心器和多尺度目标,特别是小物体的性能问题.

研究的目的:

  • 解决现有的RSI对象检测方法的局限性.
  • 提出一个端到端的框架,整合空间和频率域分析,以加强对象检测.
  • 改进在复杂的遥感场景中检测小物体和多尺度物体.

主要方法:

  • 开发了MSRS-DETR,这是一个端到端的框架,整合了空间和频率线索.
  • 引入了C2fFATNET,一个频率注意力增强的轻量级剩余骨干,用于更丰富的双域特征.
  • 包含一个纠变压器块 (ETB),用于跨域频率空间相互作用和背景干扰抑制.
  • 利用S2-CCFF,一个浅功能扩展的双向融合路径,以提高小物体细节的保留.

主要成果:

  • 与HRSC2016和ShipRSImageNet数据集的基线方法相比,MSRS-DETR显示出了显著的改进.
  • 模型参数减少了29.1%,推断速度增加了12.4%和8.4%.
  • 实现了平均平均精度 (mAP50-95) 的显著增加,分别为1.69%和2.16%,表明检测精度提高.

结论:

  • 在MSRS-DETR中提出的空间频率融合范式有效地克服了传统RSI物体探测器的局限性.
  • MSRS-DETR为遥感中的物体检测提供了更高效,更准确的解决方案,特别是在涉及小物体和杂乱物体的具有挑战性的场景中.
  • 该框架在不同的遥感数据集中显示出强大的有效性和通用性.