基于改进的YOLOv5s检测支墙裂的方法
Yanhai Wang1,2, Chenxin Guo3, Guoyong Duan1,2
1Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University), Ministry of Education, Yichang, 443002, China.
Scientific reports
|February 20, 2025
概括
这项研究引入了一种增强的YOLOv5s模型,用于检测小,不规则的支墙裂. 改进的算法提供了更好的裂定位和数量计算与更少的参数,使其非常适合基于边缘的结构健康监测.
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:
- 支墙裂带来了重大的结构性风险.
- 对于传统方法来说,检测小的,不规则的裂是具有挑战性的.
研究的目的:
- 开发一种改进的基于YOLOv5s的方法,用于准确检测支墙裂.
- 为了增强裂定位,数量计算和模型效率.
主要方法:
- 将BotNet和GhostNetV2模块集成到YOLOv5s骨干中,以提取功能和降低复杂性.
- 在Neck中使用了ODConv模块来实现多尺度的功能增强.
- 实现了旋转的边界框,以精确定位不规则的裂.
主要成果:
- 与基线YOLOv5s与旋转相比,YOLOv5s参数减少了12%,同时平均精度提高了1.5%.
- 在裂定位和数量计算方面表现出卓越的性能.
- 实现了较小的参数体积,适用于基于边缘的检测.
结论:
- 提议的增强的YOLOv5s算法是保持墙壁裂检测的有效工具.
- 该方法提供了更好的准确性,效率和本地化功能.
- 这种方法促进了关键基础设施的结构健康监测.
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