GMF-Net:一个高斯匹配的融合网络,用于在卫星激光测距图像中检测弱小物体
Wei Zhu1,2, Weiming Gong1,2, Yong Wang3
1Institute of Seismology, China Earthquake Administration, Wuhan 430071, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
在卫星激光测距 (SLR) 图像中检测小物体是高斯匹配融合网络 (GMF-Net) 改进的. 这种轻量级模型增强了特征提取,并减少了用于精确SLR目标检测的计算负载.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 卫星激光测距 (SLR) 图像分析面临着由于低信号噪声比率和复杂的背景而导致小物体检测的挑战.
- 目前的检测框架表现出高的计算成本和微小目标的不充分的特征提取.
研究的目的:
- 为SLR场景开发一种轻量级,高精度的物体探测器,解决现有方法的局限性.
- 引入一种新的高斯匹配卷积 (GMConv) 模块,用于增强特征提取和噪声抑制.
主要方法:
- 提出了高斯匹配的聚变网络 (GMF-Net),其中包括GMConv,用于精确的目标能量匹配.
- 集成的跨阶段部分金字塔卷积 (CSPPC) 以最大限度地减少参数和跨特征注意力 (CFA) 以实现多级特征集成.
- 构建了第一个专门的SLR-CCD数据集用于方法验证.
主要成果:
- 在SLR-CCD数据集上,GMF-Net实现了93.1%的mAP@50和52.4%的mAP@50-95.
- 与基线模型相比,模型参数减少了26.6% (至2.2M),计算负载减少了27.4%.
- 在检测准确度和计算效率之间取得了卓越的平衡.
结论:
- 在小型物体检测的自动化单反相机系统中,GMF-Net提供了显著的进步.
- 拟议的GMConv模块有效地增强了中心功能响应,同时减轻了背景噪声.
- 对于关键的单反相机应用,GMF-Net提供了一个高效准确的解决方案.
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