建筑施工裂纹检测与BCCD YOLO增强功能融合和注意力机制
1School of Civil Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了BCCD-YOLO,这是一种有效的算法,用于检测裸体混凝土结构中的小裂. 改进后的模型提高了3.3%的裂检测精度,有助于建筑物的安全和维护.
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
- 结构健康监测 结构健康监测
背景情况:
- 检测裸体混凝土结构中的裂对于建筑安全至关重要,但由于裂大小,纹理和数据质量的差异,这具有挑战性.
- 现有的方法难以识别小裂和精确定位,需要先进的算法方法.
研究的目的:
- 使用基于YOLO的模型开发一种有效和精确的算法,用于检测裸体混凝土结构中的小裂.
- 为了增强功能融合和提取能力,以提高裂检测准确度.
主要方法:
- 提出了一个基于YOLO的新型模型,BCCD-YOLO,用于裸体混凝土裂检测.
- 优化了路径聚合网络 (PAN) 的侧向跳转和加权特征融合,以实现多级特征集成.
- 引入了EC2f模块以通道智能关注,以及集空间和通道关注的SAC2f机制,以增强特征提取.
主要成果:
- 经过改进的BCCD-YOLO模型显示,检测裸体混凝土结构裂的精度提高了3.3%.
- 该模型有效地处理裂大小和材料质感的变化,提高检测精度.
- 通过拟议的机制,实现了增强的多尺度特征融合和特征提取能力.
结论:
- BCCD-YOLO模型在准确及及时识别建筑施工中的结构裂方面取得了重大进展.
- 在特征融合和提取方面提出的优化有助于提高裂纹检测性能.
- 这项研究提高了结构维护的效率,并有助于防止建筑物潜在的故障.
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