LIGHTWEIGHT M-CNN SOLAR CELL SURFACE DEFECT IDENTIFICATION

被引：0
|
作者：
Tao Z. [1 ]
Yi T. [1 ]
Lin S. [2 ]
Du F. [1 ]
机构：
[1] School of Electronic and Information Engineering, Liaoning Technical University, Huludao
[2] School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 06期
关键词：
convolutional neural networks; deep learning; defect identification; image processing; solar cells;
D O I：
10.19912/j.0254-0096.tynxb.2023-0309
中图分类号：
学科分类号：
摘要：
A lightweight micro convolutional neural network(M-CNN)model with high recognition rate is proposed for EL images of solar cells. The network model incorporates a fusion channel attention mechanism to merge multiple feature maps. Introducing Ghost convolutional layers to reduce the model parameters, and using ordinary convolutional layers to replace maximum pooling layers for feature space dimensionality reduction. Experimental results show that on a self-built database of 15767 EL images of cracks, shadows,minor defects,and no defects,M-CNN achieves an accuracy of 99.83% and 93.38% for rough classification and flaw classification detection,respectively,with a model parameter count of 1.29 MB. Notably,compared to advanced networks such as MobileNetV3,DeepVit,and MobileVit,M-CNN has the advantages of superior defect recognition rate and lower model parameter count. © 2024 Science Press. All rights reserved.
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页码：341 / 348
页数：7
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