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在民用基础设施中实时监控裂的轻量级框架

Vindhyesh Pandey¹, S S Mishra¹

¹Department of Civil Engineering, National Institute of Technology Patna, Bihar 800005, India.

|January 23, 2026

概括

这项研究定制了YOLOv11用于混凝土裂检测,提高了速度和准确性. 改进后的模型为结构健康监测提供了一个计算效率高的解决方案.

科学领域:

土木工程土木工程是指土木工程.
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

在混凝土结构中手动检测裂是劳动密集型的,容易出错.
像YOLO (你只看一次) 这样的自动化技术提供了更高的准确性和效率.
在YOLO系列 (YOLOv4-YOLOv11) 的进步旨在提高对象检测的性能.

研究的目的:

将YOLOv11定制为增强的混凝土裂检测.
提高结构监测中裂识别的速度,准确性和回忆.
为实时应用程序开发一个计算高效和强大的模型.

主要方法:

使用数据增强,超参数优化和转移学习来定制YOLOv11.
在包括SDNET2018在内的复合数据集上进行培训和验证.
对基线模型YOLOv5,YOLOv8,YOLOv9,YOLOv10和YOLOv11.1进行比较分析.

主要成果:

实现了平均平均精度 (mAP50) 的68.6%,超过YOLOv11的3.47%.
获得了80.8%的精度和63.6%的回忆率.
与其他模型相比,使用更少的层 (100) 和GFLOP (6.3) 证明了计算效率.

结论:

定制的YOLOv11模型对于混凝土裂检测非常有效.
该模型具有计算效率,适合实时结构健康监测.
它在具有挑战性的条件下表现出强性,使其成为基础设施评估的宝贵工具.

关键词:

在美国,CNN是CNN.混凝土结构是混凝土结构.裂纹检测裂纹检测裂纹检测裂纹检测深度学习是一种深度学习.这就是SHM SHM.这就是YOLOv11的意义.

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