一个轻量级的功能融合网络用于遥感中弱小目标检测
Zhenyuan Wu1,2, Ning Li1, Zhengyu Tian3
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China.
Scientific reports
|March 13, 2026
概括
GSS-YOLO是一个新的轻量级网络,旨在用于远程传感图像中的物体检测. 它提高了在复杂场景中识别小目标的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 遥感图像对物体检测提出了挑战,包括广的视野,复杂的背景和密集的小目标.
- 传统的物体检测方法在这些苛刻的遥感环境中往往是无效的.
研究的目的:
- 开发一个适合远程传感应用的轻量级高效物体检测网络.
- 在复杂的遥感场景中提高小目标检测的准确性和稳定性.
主要方法:
- 推出了GSS-YOLO,这是一个轻量级的网络,将空间信息聚合 (SIA) 模块集成到跨阶段部分网络 (C3) 中.
- 集成的空间金字塔扩展卷积 (SPD-Conv),以提高低分辨率输入的适应性.
- 在检测头前嵌入了一个全局上下文感知模块 (GCAM),用于精细的多尺度特征表示.
主要成果:
- 在USOD,VisDrone2019和DIOR数据集上,GSS-YOLO展示了卓越的精度,回忆和稳定性.
- 该网络保持了轻量级的架构,同时在色彩和灰度图像中实现了高性能.
- 废弃性研究验证了拟议模块的有效性.
结论:
- 在复杂的遥感中,GSS-YOLO为小型目标检测提供了一种高效,强大的解决方案.
- 集成的SIA,SPD-Conv和GCAM模块有助于提高检测精度和处理效率.
- 这种方法解决了传统方法在遥感物体检测中的关键局限性.
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