基于T-VGG的太阳电池片缺陷检测

doi:10.19912/j.0254-0096.tynxb.2020-1105

太阳能学报 ›› 2022, Vol. 43 ›› Issue (7): 145-151.DOI: 10.19912/j.0254-0096.tynxb.2020-1105

基于T-VGG的太阳电池片缺陷检测

陶志勇, 杜福廷, 任晓奎, 林森

辽宁工程技术大学电子与信息工程学院,葫芦岛 125105

收稿日期:2020-10-19 出版日期:2022-07-28 发布日期:2023-01-28
通讯作者: 陶志勇（1978—）,男,博士、教授,主要从事图像处理方面的研究。xyzmail@126.com
基金资助:
国家重点研发计划（2018YFB1403303）

DEFECT DETECTION OF SOLAR CELLS BASED ON T-VGG

Tao Zhiyong, Du Futing, Ren Xiaokui, Lin Sen

School of Electronic & Information Engineering, Liaoning Technical University, Huludao 125105, China

Received:2020-10-19 Online:2022-07-28 Published:2023-01-28

摘要/Abstract

摘要： 针对太阳电池片EL图像,提出一种融合注意力机制和Ghost卷积层并引入批标准化的T-VGG轻量级卷积神经网络模型。首先使用Ghost卷积层替换常规卷积层,其次引入注意力机制和批规范化,进而实现对电池片缺陷的高精高速检测。实验结果表明,该卷积神经网络模型对缺陷的检测准确率为99.15%,对缺陷类型的检测准确率为96.28%,检测时间为0.032 s/张,在保证高精高效性的同时兼具通用性。

关键词: 深度学习, 卷积神经网络, 太阳电池, 缺陷检测, T-VGG

Abstract: A light-weight convolutional neural network model with batch standardized T-VGG （Tiny Visual Geometry Group） was proposed to integrate attention mechanism and Ghost block into the EL image of solar cells. Using of Ghost convolutional layer to replace the conventional convolutional layer, followed by the introduction of attention and batch standardization, so as to achieve high precision and high-speed detection of battery defects. The experimental results show that the accuracy of the convolutional neural network model for defect detection is 99.15%, The detection accuracy of defect type is 96.28%, and the time is 0.032 s/ piece, which not only ensures high precision and high efficiency, but also has universality.

Key words: deep learning, convolutional neural network, solar cells, defect detection, T-VGG

中图分类号:

TM914.4

陶志勇, 杜福廷, 任晓奎, 林森. 基于T-VGG的太阳电池片缺陷检测[J]. 太阳能学报, 2022, 43(7): 145-151.

Tao Zhiyong, Du Futing, Ren Xiaokui, Lin Sen. DEFECT DETECTION OF SOLAR CELLS BASED ON T-VGG[J]. Acta Energiae Solaris Sinica, 2022, 43(7): 145-151.

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基于T-VGG的太阳电池片缺陷检测

DEFECT DETECTION OF SOLAR CELLS BASED ON T-VGG

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