Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network

The defects of solar cell component (SCC) will affect the service life and power generation efficiency. In this paper, the defect images of SCC were taken by the photoluminescence (PL) method and processed by an advanced lightweight convolutional neural network (CNN). Firstly, in order to solve the...

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Autores principales:	Huaiguang Liu, Wancheng Ding, Qianwen Huang, Li Fang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Renewable energy sources TJ807-830
Acceso en línea:	https://doaj.org/article/ff60b2dc552d40a1b29711d881afa673
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id	oai:doaj.org-article:ff60b2dc552d40a1b29711d881afa673
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spelling	oai:doaj.org-article:ff60b2dc552d40a1b29711d881afa6732021-11-29T00:55:58ZResearch on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network1687-529X10.1155/2021/7272928https://doaj.org/article/ff60b2dc552d40a1b29711d881afa6732021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7272928https://doaj.org/toc/1687-529XThe defects of solar cell component (SCC) will affect the service life and power generation efficiency. In this paper, the defect images of SCC were taken by the photoluminescence (PL) method and processed by an advanced lightweight convolutional neural network (CNN). Firstly, in order to solve the high pixel SCC image detection, each silicon wafer image was segmented based on local difference extremum of edge projection (LDEEP). Secondly, in order to detect the defects with small size or weak edges in the silicon wafer, an improved lightweight CNN model with deep backbone feature extraction network structure was proposed, as the enhancing feature fusion layer and the three-scale feature prediction layer; the model provided more feature detail. The final experimental results showed that the improved model achieves a good balance between the detection accuracy and detection speed, with the mean average precision (mAP) reaching 87.55%, which was 6.78% higher than the original algorithm. Moreover, the detection speed reached 40 frames per second (fps), which meets requirements of precision and real-time detection. The detection method can better complete the defect detection task of SCC, which lays the foundation for automatic detection of SCC defects.Huaiguang LiuWancheng DingQianwen HuangLi FangHindawi LimitedarticleRenewable energy sourcesTJ807-830ENInternational Journal of Photoenergy, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Renewable energy sources TJ807-830
spellingShingle	Renewable energy sources TJ807-830 Huaiguang Liu Wancheng Ding Qianwen Huang Li Fang Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network
description	The defects of solar cell component (SCC) will affect the service life and power generation efficiency. In this paper, the defect images of SCC were taken by the photoluminescence (PL) method and processed by an advanced lightweight convolutional neural network (CNN). Firstly, in order to solve the high pixel SCC image detection, each silicon wafer image was segmented based on local difference extremum of edge projection (LDEEP). Secondly, in order to detect the defects with small size or weak edges in the silicon wafer, an improved lightweight CNN model with deep backbone feature extraction network structure was proposed, as the enhancing feature fusion layer and the three-scale feature prediction layer; the model provided more feature detail. The final experimental results showed that the improved model achieves a good balance between the detection accuracy and detection speed, with the mean average precision (mAP) reaching 87.55%, which was 6.78% higher than the original algorithm. Moreover, the detection speed reached 40 frames per second (fps), which meets requirements of precision and real-time detection. The detection method can better complete the defect detection task of SCC, which lays the foundation for automatic detection of SCC defects.
format	article
author	Huaiguang Liu Wancheng Ding Qianwen Huang Li Fang
author_facet	Huaiguang Liu Wancheng Ding Qianwen Huang Li Fang
author_sort	Huaiguang Liu
title	Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network
title_short	Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network
title_full	Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network
title_fullStr	Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network
title_full_unstemmed	Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network
title_sort	research on online defect detection method of solar cell component based on lightweight convolutional neural network
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ff60b2dc552d40a1b29711d881afa673
work_keys_str_mv	AT huaiguangliu researchononlinedefectdetectionmethodofsolarcellcomponentbasedonlightweightconvolutionalneuralnetwork AT wanchengding researchononlinedefectdetectionmethodofsolarcellcomponentbasedonlightweightconvolutionalneuralnetwork AT qianwenhuang researchononlinedefectdetectionmethodofsolarcellcomponentbasedonlightweightconvolutionalneuralnetwork AT lifang researchononlinedefectdetectionmethodofsolarcellcomponentbasedonlightweightconvolutionalneuralnetwork
_version_	1718407748932075520

Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network

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