使用混合视觉变压器进行智能城市基础设施监测
Rashid Nasimov1, Young Im Cho1
1Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了用于结构健康监测 (SHM) 的新深度学习模型,可以准确地检测基础设施中的微裂. 视觉局部特征探测器 (ViLFD) 在识别微妙的结构缺陷方面实现了最先进的性能.
科学领域:
- 土木工程
- 计算机科学
- 人工智能
背景情况:
- 目前的结构健康监测 (SHM) 方法效率低,劳动密集,易出错,特别是在检测微裂时.
- 细微的结构异常需要先进的检测技术以确保基础设施的安全性和寿命.
- 深度学习为城市基础设施的自动和精确缺陷识别提供了潜力.
研究的目的:
- 为可靠的结构健康监测 (SHM) 开发一个新的深度学习框架.
- 增强缺陷检测能力, 特别是微小的异常如微裂.
- 建立自动化结构缺陷识别的新技术.
主要方法:
- 开发了一个改进的检测变压器 (DETR) 架构,集成了一个视觉变压器 (ViT) 骨干.
- 一个局部特征提取器 (LFE) 模块旨在增强复杂的局部空间特征的提取.
- 视觉局部特征探测器 (ViLFD) 模型在基准和定制数据集上进行了训练和验证.
主要成果:
- ViLFD模型表现出比现有的DETR和YOLO变种更高的性能.
- 获得了高准确度 (95.0%),精度 (0.94),回忆 (0.93),F1得分 (0.93) 和mAP@0.5 (0.89).
- 在实验验证中成功和可靠地检测出微妙的结构缺陷.
结论:
- 拟议的基于ViT的DETR框架与LFE模块显著提升了SHM能力.
- ViLFD模型为自动检测结构缺陷提供了精确可靠的解决方案.
- 这是迈向更安全,更可靠的城市基础设施的重要一步.
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