使用改进的DRA-UNet,自动识别和分析采矿产生的表面裂的特征
Weiwei Zhou1, Youfeng Zou2, Huabin Chai1
1Henan Polytechnic University, Jiaozuo, 454000, China.
Scientific reports
|January 28, 2026
概括
本研究引入了一种改进的DRA-UNet模型,用于使用无人机图像精确检测采矿引起的表面裂. 该方法增强了裂分析,为地面稳定性和矿山安全提供了强大的自动化解决方案.
科学领域:
- 地质科学 地质科学
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- 采矿引起的表面裂是地面变形和安全风险的关键指标.
- 由于精细的几何形状和背景噪声,自动破裂检测具有挑战性.
研究的目的:
- 开发一种高精度的自动化方法,用于检测和分析采矿引起的表面裂.
- 提高对裂形态和空间分布的理解,以提高矿山安全.
主要方法:
- 一个改进的DRA-UNet模型,结合了残留学习,双重注意力机制 (DAM) 和异位空间金字塔聚合 (ASPP).
- 裂纹细分与骨架提取和定量几何特征的整合.
- 在矿区无人机数据集和Crack500数据集上进行验证.
主要成果:
- 在采矿数据集上,DRA-UNet模型取得了高性能 (F1=71.60%,MIoU=70.00%),在Crack500.00上表现出强大的泛化 (F1=85.32%,MIoU=83.69%).
- 废弃性研究证实了单个模型组件的有效性.
- 几何分析揭示了工作面附近的裂特征和空间模式.
结论:
- 拟议的框架为采矿引起的裂检测和形态分析提供了一个强大的,完全自动化的解决方案.
- 这些发现有助于改善地面稳定性评估和矿山安全管理.
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