在基于YOLOv11的遥感图像中对物体检测和识别的研究
Lu-Hao He1,2,3, Yong-Zhang Zhou4,5,6, Lei Liu1,2,3
1School of Earth Sciences and Engineering, Sun Yat-Sen University, Zhuhai, 519000, China.
Scientific reports
|April 24, 2025
概括
YOLOv11模型显著提高了遥感图像中的地面物体检测,实现了多类和多物体识别的高精度和可靠性. 这证明了它在智能遥感应用中的潜力.
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:
- 在高分辨率遥感图像中精确检测地面物体对于各种应用至关重要.
- 现有的方法在实现高精度和效率方面面临挑战,特别是在复杂的场景中.
研究的目的:
- 评估YOLOv11模型在高分辨率遥感图像中用于地面物体目标检测的性能.
- 评估其在提高检测准确性和效率方面的潜力.
主要方法:
- 在20个目标类别的70,389个样本的数据集上训练YOLOv11模型.
- 使用损失函数 (Box_Loss,Cls_Loss,DFL_Loss) 并使用精度,回忆,map50,map50-95 和 F1 评分来评估性能.
主要成果:
- 该模型实现了高性能指标:精度 (0.8861),回忆 (0.8563),地图50 (0.8920),地图50-95 (0.8646) 和F1得分 (0.8709).
- 损失函数的快速融合表明了有效的优化.
- 80%的测试样本达到超过85%的信心分数,证实了可靠性.
结论:
- YOLOv11显示了远程传感图像目标检测的重大前景,提供高精度和稳定性.
- 该模型为智能遥感图像分析提供了强大的技术支持.
- 未来的工作将集中在数据集扩展,模型改进和小目标和复杂场景的性能改进上.
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