摘要
首先开展了钢筋混凝土(RC)柱抗震试验,在此基础上考虑外部数据,建立了RC构件震后损伤数据集。然后采用FasterNet网络代替YOLOv5模型的骨干网络,并在Neck网络中引入C3Ghost模块和GhostConv,提出了一种轻量级模型FG-YOLOv5。对该模型进行了训练和测试,并开展了消融试验,最后将其部署至智能手机上,实现了RC构件震后损伤的快速检测。结果表明:相对于普通卷积,部分卷积及GhostConv可以大大地降低计算量;引入FasterNet网络、C3Ghost模块及Ghostconv对YOLOv5模型进行轻量化,可在检测精度提高的情况下,大大降低模型大小及计算量;该研究提出的FG-YOLOv5模型,可方便地部署在手机上,实现RC构件震后损伤快速检测。
In this paper,seismic tests of reinforced concrete(RC)column were conducted,based on which the post-earthquake damage dataset of RC member was established considering the additional data obtained externally.A lightweight model,i.e.FG-YOLOv5,was then proposed by replacing the backbone of YOLOv5 model with the FasterNet network and introducing the C3Ghost module and GhostConv in the neck of YOLOv5 model.Based on the dataset,the FG-YOLOv5 model was trained and tested and the ablation test was also carried out.Finally,the model was deployed on a smartphone to achieve rapid post-earthquake damage detection of RC members.The results showed that,compared to the conventional convolution,the partial convolution and GhostConv can greatly reduce the computational cost.By introducing the FasterNet,C3Ghost module and Ghostconv into the YOLO v5 model,greatly smaller model size and computation with a bit higher detection accuracy can be achieved.The FG-YOLOv5 model proposed in this paper can be conveniently deployed on mobile phones for rapid post-earthquake damage detection of RC members.
作者
陈梓潇
宋成浩
胡晓斌
CHEN Zixiao;SONG Chenghao;HU Xiaobin(School of Civil Engineering,Wuhan University,Wuhan 430072,China)
出处
《工业建筑》
2025年第7期143-151,共9页
Industrial Construction
基金
国家自然科学基金项目(51578429)
关键词
钢筋混凝土构件
震后损伤
快速检测
YOLO模型
轻量化
reinforced concrete member
post-earthquake damage
fast detection
YOLO model
lightweight
