通过改进的YOLOv8模型,提高远程传感图像中的对象检测
Zhonghe Hu1, Wenwu Chen1, Dongsheng Yang2,3
1Northwest Institute of Nuclear Technology, Xian, 710600, China.
Scientific reports
|November 27, 2025
概括
这项研究增强了YOLOv8模型用于遥感对象检测,通过集成动态卷积,路由注意力和特征金字塔网络来提高准确性. 增强的模型在复杂的数据集上实现了卓越的性能.
科学领域:
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 在遥感图像中对象检测面临挑战,原因是复杂的背景,多样化的外观和不同的物体尺寸.
- 密集分布的目标和尺度变化使得空中图像中精确的检测变得复杂.
- 现有的模型在与遥感数据的复杂性作斗争.
研究的目的:
- 增强YOLOv8模型,以改善远程传感应用中的物体检测.
- 解决诸如复杂的背景,尺度变化和物体密度分布等挑战.
- 为了提高遥感图像分析的检测准确性和效率.
主要方法:
- 在C2F模块中集成动态卷积 (DyConv),以适应调整过器以适应尺寸和外观变化.
- 实施双层路由注意 (BRA) 来改进高层特征,抑制背景噪声,并增强特征相关性.
- 采用非对称特征金字塔网络 (AFPN) 进行优质的多尺度特征融合,将低级细节与高级语义相结合.
主要成果:
- 增强的YOLOv8模型在远程传感物体检测 (RSOD) 数据集上获得了65.4%的mAP50-95得分,比原始模型提高了3.3%.
- 与主流单阶段,双阶段和DETR物体检测模型相比,表现出显著的性能提升.
- 保持计算效率,同时提高检测准确度.
结论:
- 提议的改进有效地解决了远程传感图像中对象检测的复杂性.
- 集成DyConv,BRA和AFPN提供了一个强大的解决方案,用于准确和高效的远程传感对象检测.
- 改进的YOLOv8模型代表了远程传感图像分析领域的重大进展.
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