您当前的位置：
首页 >
文章列表页 >
Solar cell defect detection network combining multiscale feature and attention
Information Sciences | 更新时间：2024-09-03
|

    • Solar cell defect detection network combining multiscale feature and attention

    • “在太阳能电池检测领域，CMFAnet算法通过融合多尺度特征与注意力机制，显著提升了表面缺陷检测精度，平均检测精度达到91.4%。”
    • Optics and Precision Engineering   Vol. 32, Issue 14, Pages: 2286-2298(2024)

    • DOI：10.37188/OPE.20243214.2286    

      CLC： TP391.41

    • Published：25 July 2024，

      Received：04 December 2023，

      Revised：20 February 2024，

    移动端阅览

  • ZHOU Ying,XU Shibo,CHEN Haiyong,et al.Solar cell defect detection network combining multiscale feature and attention[J].Optics and Precision Engineering,2024,32(14):2286-2298. DOI： 10.37188/OPE.20243214.2286.

  •  
  •  

0

Views

9

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

Improving the lightweight VTG-YOLOv7-tiny for steel surface defect detection
Object detection of steel surface defect based on multi-scale enhanced feature fusion
Image super-resolution network based on multi-scale adaptive attention
Review of defect detection algorithms for solar cells based on machine vision
Multispectral image fusion method for surface defect detection of IC devices

Related Author

LU Baohe
FENG Yao
LONG Pengwei
LIANG Liming
LIN Shanling
PENG Xueling
WANG Dong
LIN Zhixian

Related Institution

School of Electrical Engineering and Automation，Jiangxi University of Science and Technology
School of Physics and Information Engineering， Fuzhou University
China Fujian Photoelectric Information Science and Technology Innovation Laboratory
School of Advanced Manufacturing， Fuzhou University
College of Electronic and Information Engineering， Nanjing University of Aeronautics and Astronautics
0