基于上下文自编码器-顺序层状耦合信息框架的设施表面缺陷多粒度识别与安全评价

Multi-Grained Recognition of Facility Surface Defects and Safety Assessment Based on CAE-SHCIF

摘要:

提出了一种基于CAE_ViT网络模型和顺序层状耦合信息框架（sequential hierarchical coupled information framework， SHCIF）的多粒度多缺陷图像分类识别方法，并结合模糊综合评价（FCE）方法，以桥梁设施为例，对其表面缺陷进行细致的分类及安全评价。首先，研究提出了SHCIF及对应3个层次粒度的识别模型，并构建和增强了对应不同粒度的数据集。SHCIF框架和跨粒度分类决策旨在通过利用桥梁组件和缺陷类型这两个粒度的信息和准确性，提升对缺陷严重程度的识别。其次，使用迁移学习对CAE_ViT预训练模型进行微调，以满足桥梁缺陷检测的具体需求，并通过跨粒度分类决策进一步提升分类的准确性。最后，基于层次分析法与熵权法（AHP-EWM）的权重体系，通过模糊综合评价，综合考虑桥梁部位、桥梁组件、缺陷类型及其严重程度，实现了基于表观健康状态对桥梁安全状态等级的定量评价。实验结果显示，在3个层次粒度的识别模型中的宏平均F₁-Score分数分别达到94.1%、81.6%和75.3%，而跨粒度分类决策的准确率为82%。最终通过一个桥梁的安全评价案例证明了方法的有效性、系统性和可拓展性。

Abstract:

A multi-granularity defect recognition and safety assessment method is proposed for facility surfaces， using bridges as a representative example. The approach integrates a CAE_ViT network model with a sequential hierarchical coupled information framework （SHCIF） and a fuzzy comprehensive evaluation （FCE） system. First， the SHCIF and three corresponding granularity-specific recognition models are established， with datasets constructed and augmented for each granularity level. The SHCIF and cross-granularity classification strategy are designed to enhance defect severity recognition accuracy by leveraging information from both bridge component and defect type granularities. Second， transfer learning is applied to fine-tune the CAE_ViT pre-trained model for bridge defect detection， with classification performance further improved through cross-granularity decision-making. Finally， an analytic hierarchy process-entropy weight method （AHP-EWM） weighting system is incorporated into the FCE to achieve quantitative safety assessment of bridges based on apparent surface conditions， considering bridge locations， components， defect types， and severity levels. Experimental results show macro-average F1-scores of 94.1%， 81.6%， and 75.3% for the three granularity levels， respectively， with cross-granularity classification reaching 82% accuracy. A case study on bridge safety evaluation demonstrates the effectiveness， systematicness， and extensibility of the method.