基于计算机视觉的腐蚀检测和特征提取用于岩石螺栓
Shucan Lu1, Saisai Wu1, Xinxin Ma1
1Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering, School of Resources Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.
Materials (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了一种自动化方法,用于通过图像处理和深度学习来检测岩石螺栓腐蚀. 该技术准确量化腐蚀,增强结构安全和维护策略.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 岩石螺栓腐蚀对工程安全和基础设施寿命构成重大风险.
- 有效的监测和维护对于减轻这些风险至关重要.
研究的目的:
- 开发一种自动岩石螺栓腐蚀检测的综合方法.
- 用先进的计算方法提高腐蚀评估的准确性和可靠性.
主要方法:
- 利用计算机视觉对象检测算法与特征金字塔网络进行增强的多层次检测.
- 采用图像二元化和灰度矩阵分析来提取特征.
- 应用分数布朗运动建模用于腐蚀形态模拟和分数特征计算.
主要成果:
- 自动化模型准确地识别和量化了腐蚀特征,包括孔密度.
- 碎形尺寸随着腐蚀时间显著增加,作为表面粗度和腐蚀速率的可靠指标.
- 该研究通过形态模拟验证了碎形特征计算的准确性.
结论:
- 综合图像处理,深度学习和碎形理论的综合框架为岩石螺栓腐蚀监测提供了强大的解决方案.
- 这种方法提供了一种可靠的定量方法来评估金属腐蚀,改进维护策略和确保工程安全.
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