在InSAR测量中用于大面积滑坡检测的InSAR-YOLOv8
Ruopu Ma1, Haiyang Yu2,3, Xuejie Liu1
1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China.
Scientific reports
|January 10, 2025
概括
一个新的InSAR-YOLOv8算法增强了从Interferometric合成孔径雷达 (InSAR) 测量中的山体滑坡检测. 这种人工智能模型提高了关键的山体滑坡危险监测的准确性和效率.
科学领域:
- 地质科学 地质科学
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 干涉测量合成孔径雷达 (InSAR) 对于评估山体滑坡危险至关重要.
- 有效地从使用物体检测的广域InSAR测量中提取滑坡数据是非常重要的.
- 现有的模型在从InSAR数据中检测山体滑坡的准确性和性能方面扎.
研究的目的:
- 开发一个先进的物体检测算法InSAR-YOLOv8,用于在InSAR测量中自动检测山体滑坡.
- 与现有方法相比,提高滑坡检测的准确性和效率.
- 为了改善各种规模的山体滑坡的检测.
主要方法:
- 引入了针对小尺寸物体的专用检测头,以改善多尺寸特征提取.
- 用更高效的C2f_Faster模块取代C2f模块,以实现更轻,更快的处理.
- 实现了SIoU损失函数,以完善界限框回归并提高检测准确度.
主要成果:
- 在InSAR山体滑坡数据集上获得了97.41%的mAP50,66.47%的mAP50:95和92.06%的F1得分.
- 模型参数减少了25%,表明效率提高.
- 在YOLOv8,YOLOvX和更快的R-CNN上表现出优越的性能.
结论:
- InSAR-YOLOv8从InSAR数据中显著提升了自动化滑坡检测.
- 该模型为滑坡危险监测提供了更高的准确性,效率和可扩展性.
- 这项技术在基于InSAR的山体滑坡分析中具有广泛的潜在应用.
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