您当前的位置：
首页 >
文章列表页 >
Defect recognition of lightweight bridges based on YOLOv5
更新时间：2025-11-14

    • Defect recognition of lightweight bridges based on YOLOv5

    • “The YOLOv5 algorithm, combined with FasterNet and CBAM, improves the accuracy and mAP of bridge defect detection, providing technical support for the digital transformation of bridge maintenance management.”
    • Journal of Jilin University(Engineering and Technology Edition)   Vol. 55, Issue 9, Pages: 2958-2968(2025)
    • 作者机构：

      1.吉林大学 交通学院，长春 130012

      2.吉林省交通运输综合行政执法局，长春 130012

    • DOI：10.13229/j.cnki.jdxbgxb.20250539    

      CLC： U447

    • Received：19 June 2025，

      Published：01 September 2025

    移动端阅览

  • WANG Lin-hong,LIU Yu-yang,LIU Zi-yu,et al.Defect recognition of lightweight bridges based on YOLOv5[J].Journal of Jilin University(Engineering and Technology Edition),2025,55(09):2958-2968. DOI： 10.13229/j.cnki.jdxbgxb.20250539.

  •  
  •  

0

Views

8

下载量

0

CSCD

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

High precision detection system for automotive paint defects
Automatic traffic state recognition from videos based on auto⁃encoder and classifiers
Visual recognition of excavator keypoints based on synthetic image datasets
Heterogeneity analysis of residents’ transfer intentions under transit transfer preferential policy
Semantic similarity model based on augmented positives and interlayer negatives

Related Author

YUAN Shuai-ke
ZHU Shao-peng
ZHANG Ning
LU Yu-kai
XIONG Shu-sheng
PENG Bo
ZHANG Yuan-yuan
WANG Yu-ting

Related Institution

Power Machinery & Vehicular Engineering Institute，Zhejiang University
Zhejiang Geely Automobile Co.，Ltd.
College of Mechanical Engineering，Yanshan University
Longquan Industrial Innovation Research Institute，Longquan
Chongqing Key Lab of Traffic System & Safety in Mountain Cities， Chongqing Jiaotong University
0