用新的数据集在SAR图像中检测小物体的YOLO变体的有效性
Kinga Karwowska1, Jakub Slesinski1, Damian Wierzbicki2
1Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, Warsaw, Poland.
Scientific reports
|November 25, 2025
概括
本研究引入了用于合成孔径雷达 (SAR) 图像中小型物体检测的新数据集,并比较了YOLOv7,v8和v12模型. YOLOv8在SAR图像中检测小型车辆方面表现出卓越的性能.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在合成孔径雷达 (SAR) 图像中检测小物体是由于缺乏专门数据集而受到阻碍的.
- 现有的数据集不足以解决卫星雷达图像中的车辆检测问题.
研究的目的:
- 为小物体检测提供一个新的SAR数据集.
- 进行对YOLOv7,YOLOv8和YOLOv12架构进行比较分析,以检测SAR图像中的小型车辆.
- 评估图像过技术对检测性能的影响.
主要方法:
- 从Capella和ICEYE图像中创建一个定制数据集,包含23,644辆手动标记的车辆.
- 在定制数据集和SIVED数据集上对YOLOv7,YOLOv8和YOLOv12模型进行比较评估.
- 使用Lee,Frost和GammaMAP过器评估检测效率.
- 分析模型稳定性和对参数调节的敏感性.
主要成果:
- 在没有过的情况下,YOLOv8在SIVED数据集 (F1得分:0.958,mAP:0.838) 上取得了最高的性能.
- YOLOv12超越了后李过 (F1分数:0.951,mAP:0.774),显示了对输入数据质量的敏感性.
- 由于对置信值的高度敏感性,YOLOv7需要精确的参数调整.
结论:
- 在SAR图像中,YOLOv8为小型车辆提供了卓越的检测性能,即使没有先进的注意力机制.
- 该研究提供了选择和配置用于基于SAR的物体检测的YOLO模型的实用指南.
- 数据集的创建和模型的评估为雷达图像中的自动物体检测提供了重要的贡献.
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