PSMDet:通过自我调制和基于高斯的回归来提高遥感图像的检测精度
Jiangang Zhu1, Yang Ruan2, Donglin Jing2,3
1School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
这项研究引入了进步自我调节探测器 (PSMDet),以改善光学遥感图像中的物体检测. 对于复杂的目标,PSMDet增强了特征提取和界限框回归,实现了高精度.
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:
- 传统的物体检测在光学遥感图像 (ORSI) 中与多尺度,高比例和任意定向的目标作斗争.
- 对于复杂的ORSI目标,现有的方法面临特征提取和界限框回归方面的挑战.
研究的目的:
- 提出一种新的检测框架,即进步自调节检测器 (PSMDet),以解决ORSI对象检测的局限性.
- 为了增强特征提取,对齐和界限框回归在ORSI中复杂的目标.
主要方法:
- 开发了PSMDet,在骨干,特征金字塔网络 (FPN) 和检测头阶段中结合了自我调节.
- 使用重定量化大内核网络 (RLK-Net) 进行增强的多尺度特征提取.
- 引入了一个自适应感知网络 (APN),用于特征对齐的自我注意,基于高斯的边界框表示,以及回归的光滑相对 (smoothRE) 损失.
主要成果:
- 在HRSC2016和UCAS-AOD数据集上,PSMDet实现了高性能,平均精度 (mAP) 分别为90.69%和89.86%.
- 该框架在ORSI中检测复杂目标方面表现强.
结论:
- PSMDet为ORSI提供了对象检测的重大进步,解决了关键挑战.
- 拟议的框架可适应各种需要高精度物体检测的应用,包括自动驾驶和工业缺陷检测.
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