符合规范的断裂参数提取和道面上的岩石质量分类,采用改进的YOLOv8-seg
Ziang Wang1, Liming Zhou2, Daiguang Fu3
1Key Laboratory of Geotechnical Mechanics and Engineering of Ministry of Water Resources, Changjiang River Scientific Research Institute, Wuhan, 430010, China.
Scientific reports
|January 17, 2026
概括
这项研究引入了一个智能框架,使用改进的YOLOv8-seg模型准确识别岩石质量断裂并提取道稳定性的参数. 该系统通过与国家标准的整合来增强工程决策.
科学领域:
- 地质技术工程 地质技术工程
- 人工智能的人工智能
- 道技术 道技术
背景情况:
- 精确的岩石质量断裂识别对于道的稳定性和建筑安全至关重要.
- 现有的深度学习方法在复杂的道环境中扎,缺乏与工程标准的整合.
- 这限制了对道项目工程决策的直接支持.
研究的目的:
- 开发一个智能框架,用于精确地识别和分类道中的岩石质量断裂.
- 将一个改进的深度学习模型与标准化的岩石质量分类标准集成在一起.
- 加强对道建设工程决策的直接支持.
主要方法:
- 开发了一种改进的YOLOv8-seg模型,采用了高效通道注意力 (ECA) 机制.
- 该框架自动提取关键断裂参数,如宽度和填充状态.
- 这些参数与中国国家标准GB 50487-2008的岩石质量分类相关.
主要成果:
- 改进的YOLOv8-seg模型在复杂的条件下显示了增强的骨折特征检测.
- 该方法实现了mAP@0.5的1.80%增加,同时保持实时处理.
- 自动分类结果与手动专家评估有很强的一致性.
结论:
- 拟议的智能框架为道工程提供了可靠和高效的工具.
- 它提高了骨折识别和分类的准确性和直接适用性.
- 这有助于提高道建设项目的安全性和决策能力.
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