深度学习和基于TOPSIS的多标准决策框架,用于城市道路缺陷检测和可持续维护规划
Zhaodong Zhong1,2, Ziyan Ren3
1College of Landscape Architecture and Art, Xinyang Agriculture and Forestry University, Xinyang, 464000, China.
Scientific reports
|December 17, 2025
概括
本研究介绍了一种改进的YOLOv8模型,用于道路缺陷检测,严重程度分类和维护优先级. 新框架加强了城市基础设施管理,以实现更安全,更可持续的交通系统.
科学领域:
- 城市规划和基础设施管理
- 交通运输中的人工智能
- 可持续的城市发展
背景情况:
- 城市道路基础设施的维护对生活质量,交通安全和可持续性至关重要.
- 传统的道路缺陷检测方法缓慢,劳动密集型,缺乏实时功能.
- 现有的模型往往无法评估缺陷严重程度或优先考虑维护,从而限制了它们的实际使用.
研究的目的:
- 开发一个先进的模型,同时检测道路缺陷,严重程度分类和维护优先级.
- 为了克服智能城市管理当前一维检测方法的局限性.
- 加强对主动和可持续的城市基础设施维护的决策.
主要方法:
- 提出了一个增强的YOLOv8模型,附带一个额外的二级头部来进行严重性分类.
- 使用TOPSIS方法集成多标准决策 (MCDM).
- 双框架模型在道路缺陷检测数据集上经过实验验证.
主要成果:
- 与MCDM一起改进的YOLOv8模型与原始YOLOv8.8相比,表现出更高的性能.
- 该框架允许同时检测,严重性估计和数据驱动的维护优先级.
- 实验结果证实了该模型对短期和长期基础设施管理策略的可行性.
结论:
- 拟议的双框架模型对现有的道路缺陷检测方法来说是一个显著的进步.
- 它促进了主动的基础设施维护,提高了城市流动性弹性.
- 该研究有助于创建更安全和可持续的城市交通系统.
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