使用 ResM-FusionNet 实现高效的山体滑坡检测算法,并采用混合架构
Xuqing Ren1, Xu Wu2, Donghao Zhai1
1College of Computers Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China.
Scientific reports
|April 16, 2025
概括
这项研究介绍了ResM-FusionNet,这是一种用于准确地检测山体滑坡的新型深度学习模型. 该方法显著改善了复杂地形的识别,这对于灾害管理和城市规划至关重要.
科学领域:
- 地质危险评估地质危险评估
- 遥感和地缘空间分析.
- 深度学习用于环境监测.
背景情况:
- 滑坡是一个重要的地质危险,影响环境和人类人口.
- 准确识别容易发生山体滑坡的地区对于有效应对灾害,风险评估和城市规划至关重要.
- 现有的山体滑坡检测方法在复杂的地形和精确的边界划分方面面临挑战.
研究的目的:
- 提出一种基于深度学习的新滑坡检测方法,ResM-FusionNet.
- 为了提高细分精度和边界细节检测在易发生山体滑坡的地区.
- 评估ResM-FusionNet的性能与现有的最先进模型相比.
主要方法:
- ResM-FusionNet使用ResNet-50进行特征提取,并使用多层感知解码器进行改进的细分.
- 引入了一个新的损失函数RLoss,它包含掩盖,语义权重和Top-K像素平均值.
- 该模型在遥感数据集上进行了训练和验证.
主要成果:
- ResM-FusionNet的准确率达到了94.33%,F1得分达到了85.73%,卡帕系数达到了70.12%.
- 该模型在准确性方面大大超过了SegFormer,DeepLabv3和UNet.
- 在边界检测中观察到出色的性能,IOU为0.7545,精度为85.61%,回忆率为83.92%.
结论:
- ResM-FusionNet提供了一个强大的,准确的解决方案,用于发现山体滑坡.
- 拟议的方法表现出卓越的性能,特别是在复杂的地形和详细的边界细分.
- 这一进展对于改善地质危险管理和空间规划至关重要.
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