基于深度学习方法的预制缺陷岩石中裂纹尖端的自动识别和特征分析
Mingtao Gao1,2,3, Minhui Li1,2, Lu Chen1,2
1School of Emergency Technology and Management, North China Institute of Science & Technology, Langfang, China.
PloS one
|July 15, 2025
概括
这项研究引入了一种自动化方法,用于使用U-Net模型识别岩石裂纹尖端,达到99.4%的准确性. 这一进步改善了在复杂的地质环境中对岩石质量稳定性的评估.
科学领域:
- 地质技术工程 地质技术工程
- 岩石机械学 岩石机械学
- 图像分析 图像分析
背景情况:
- 岩石质量的稳定性对于工程安全至关重要,但手动裂分析是主观和低效的.
- 需要自动化,高精度的裂检测,特别是岩石样本中的多角度裂.
- 图像质量和算法适应性是准确识别裂的关键挑战.
研究的目的:
- 开发一种高精度的自动化方法来识别岩石裂尖端.
- 评估用于裂纹识别的图像预处理技术和深度学习模型.
- 建立多角度岩石裂的数据集,用于模型培训和验证.
主要方法:
- 带有预制裂纹 (0-60°) 的砂岩样本经过三点曲.
- 一个超快的摄像头捕获了微秒级裂传播.
- 在图像预处理中使用了对比度有限的自适应基因图平衡 (CLAHE).
- 深度学习模型Deeplabv3和U-Net在像素级注释数据集上进行训练.
主要成果:
- 与HE和AHE相比,CLAHE表现出优越的预处理性能.
- 该U-Net模型实现了99.4%的识别精度,97.3%的精度和95.6%的回忆.
- U-Net的表现优于Deeplabv3,在准确性,精度和回忆方面分别提高了0.5%,2.3%和4.3%.
结论:
- U-Net模型为自动化岩石裂尖端识别提供了一个高度准确和可靠的解决方案.
- 这种智能检测方法为复杂的地质环境中的工程决策提供了关键数据.
- 该研究推进了岩石力学和地球技术工程中的自动化分析技术.
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