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基于改进YOLO模型的钢筋混凝土构件震后损伤检测

陈梓潇 宋成浩 胡晓斌

陈梓潇, 宋成浩, 胡晓斌. 基于改进YOLO模型的钢筋混凝土构件震后损伤检测[J]. 工业建筑, 2025, 55(7): 143-151. doi: 10.3724/j.gyjzG25071002
引用本文: 陈梓潇, 宋成浩, 胡晓斌. 基于改进YOLO模型的钢筋混凝土构件震后损伤检测[J]. 工业建筑, 2025, 55(7): 143-151. doi: 10.3724/j.gyjzG25071002
CHEN Zixiao, SONG Chenghao, HU Xiaobin. Post-Earthquake Damage Detection of Reinforced Concrete Members Based on the Improved YOLO Model[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 143-151. doi: 10.3724/j.gyjzG25071002
Citation: CHEN Zixiao, SONG Chenghao, HU Xiaobin. Post-Earthquake Damage Detection of Reinforced Concrete Members Based on the Improved YOLO Model[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 143-151. doi: 10.3724/j.gyjzG25071002

基于改进YOLO模型的钢筋混凝土构件震后损伤检测

doi: 10.3724/j.gyjzG25071002
基金项目: 

国家自然科学基金项目(51578429)。

详细信息
    作者简介:

    陈梓潇,硕士研究生,主要从事结构健康监测研究,chenzixiao@whu.edu.cn。

    通讯作者:

    胡晓斌,教授,博士生导师,主要从事结构健康监测研究,newhxb@126.com。

Post-Earthquake Damage Detection of Reinforced Concrete Members Based on the Improved YOLO Model

  • 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。
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出版历程
  • 收稿日期:  2025-07-10
  • 网络出版日期:  2025-09-12

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