应用YOLOv11秒算法用于基于AI的滑坡检测和识别
Luhao He1,2,3, Yongzhang Zhou4,5,6, Lei Liu1,2,3
1Sun Yat-sen University Center for Earth Environment & Resources, Zhuhai, 519000, China.
Scientific reports
|April 11, 2025
概括
本研究介绍了一种使用YOLOv11秒深度学习模型的智能滑坡识别方法. 这种先进的模型可以提高复杂地形的检测精度和细分,帮助监测地质灾害.
科学领域:
- 地质科学 地质科学
- 人工智能的人工智能
- 地质工程是地质工程.
背景情况:
- 全球范围内的山体滑坡正在增加,造成重大损失和人员伤亡.
- 准确的山体滑坡检测对于减轻和预防灾害至关重要.
- 复杂的地形和各种滑坡特征挑战了传统的监测方法.
研究的目的:
- 开发一种使用深度学习的智能滑坡识别方法.
- 为了提高滑坡边界检测和像素级别细分精度.
- 为了应对复杂地形和各种滑坡特征所带来的挑战.
主要方法:
- 利用YOLOv11秒深度学习模型进行滑坡识别.
- 优化了YOLOv11-seg的特征提取和细分模块.
- 应用数据增强技术 (镜像,旋转,颜色调整) 以提高概括性.
主要成果:
- YOLOv11-seg在检测复杂的边界和处理阻塞方面表现出卓越的性能.
- 获得了高的F1分数:边界检测0.8781和细分0.8114.
- 在边界框和面罩细分任务中,在平均平均精度 (mAP) 中表现优于传统方法.
结论:
- 11秒的YOLOv模型提供了高可靠性和适应性,用于发现山体滑坡.
- 这项研究为智能滑坡监测和风险评估提供了先进的技术支持.
- 该方法显示了改善地质灾害监测系统的巨大潜力.
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