Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning

Dongkun Hou; Jieming Ma; Sida Huang; Jie Zhang; Xiaohui Zhu

doi:10.1109/ECCE-Asia49820.2021.9479376

Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning

Dongkun Hou, Jieming Ma, Sida Huang, Jie Zhang, Xiaohui Zhu

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

12 Citations (Scopus)

Abstract

The quality inspection of photovoltaic modules is very important to power generation efficiency. Although electroluminescence can directly detect the defects in photovoltaic modules, it is difficult to get the desirable accuracy by traditional manual inspection. It thus necessitates an efficient defect identification method. This paper introduces ImageNet-trained networks for identifying the defective Photovoltaic modules. Six convolutional neural networks are realized by transfer learning. The experimental results present the Xception model has a higher accuracy for the classification of defective photovoltaic cells.

Original language	English
Title of host publication	Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2127-2132
Number of pages	6
ISBN (Electronic)	9781728163444
DOIs	https://doi.org/10.1109/ECCE-Asia49820.2021.9479376
Publication status	Published - 24 May 2021
Event	12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021 - Virtual, Singapore, Singapore Duration: 24 May 2021 → 27 May 2021

Publication series

Name	Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

Conference

Conference	12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
Country/Territory	Singapore
City	Virtual, Singapore
Period	24/05/21 → 27/05/21

Keywords

Convolutional Neural Network
Electroluminescence images
ImageNet-trained network
Photovoltaic module
Transfer learning

Access to Document

10.1109/ECCE-Asia49820.2021.9479376

Cite this

Hou, D., Ma, J., Huang, S., Zhang, J., & Zhu, X. (2021). Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning. In Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021 (pp. 2127-2132). Article 9479376 (Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE-Asia49820.2021.9479376

Hou, Dongkun ; Ma, Jieming ; Huang, Sida et al. / Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning. Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 2127-2132 (Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021).

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title = "Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning",

abstract = "The quality inspection of photovoltaic modules is very important to power generation efficiency. Although electroluminescence can directly detect the defects in photovoltaic modules, it is difficult to get the desirable accuracy by traditional manual inspection. It thus necessitates an efficient defect identification method. This paper introduces ImageNet-trained networks for identifying the defective Photovoltaic modules. Six convolutional neural networks are realized by transfer learning. The experimental results present the Xception model has a higher accuracy for the classification of defective photovoltaic cells.",

keywords = "Convolutional Neural Network, Electroluminescence images, ImageNet-trained network, Photovoltaic module, Transfer learning",

author = "Dongkun Hou and Jieming Ma and Sida Huang and Jie Zhang and Xiaohui Zhu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021 ; Conference date: 24-05-2021 Through 27-05-2021",

year = "2021",

month = may,

day = "24",

doi = "10.1109/ECCE-Asia49820.2021.9479376",

language = "English",

series = "Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021",

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Hou, D, Ma, J, Huang, S, Zhang, J & Zhu, X 2021, Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning. in Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021., 9479376, Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021, Institute of Electrical and Electronics Engineers Inc., pp. 2127-2132, 12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021, Virtual, Singapore, Singapore, 24/05/21. https://doi.org/10.1109/ECCE-Asia49820.2021.9479376

Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning. / Hou, Dongkun; Ma, Jieming; Huang, Sida et al.
Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2127-2132 9479376 (Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning

AU - Hou, Dongkun

AU - Ma, Jieming

AU - Huang, Sida

AU - Zhang, Jie

AU - Zhu, Xiaohui

PY - 2021/5/24

Y1 - 2021/5/24

N2 - The quality inspection of photovoltaic modules is very important to power generation efficiency. Although electroluminescence can directly detect the defects in photovoltaic modules, it is difficult to get the desirable accuracy by traditional manual inspection. It thus necessitates an efficient defect identification method. This paper introduces ImageNet-trained networks for identifying the defective Photovoltaic modules. Six convolutional neural networks are realized by transfer learning. The experimental results present the Xception model has a higher accuracy for the classification of defective photovoltaic cells.

AB - The quality inspection of photovoltaic modules is very important to power generation efficiency. Although electroluminescence can directly detect the defects in photovoltaic modules, it is difficult to get the desirable accuracy by traditional manual inspection. It thus necessitates an efficient defect identification method. This paper introduces ImageNet-trained networks for identifying the defective Photovoltaic modules. Six convolutional neural networks are realized by transfer learning. The experimental results present the Xception model has a higher accuracy for the classification of defective photovoltaic cells.

KW - Convolutional Neural Network

KW - Electroluminescence images

KW - ImageNet-trained network

KW - Photovoltaic module

KW - Transfer learning

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U2 - 10.1109/ECCE-Asia49820.2021.9479376

DO - 10.1109/ECCE-Asia49820.2021.9479376

M3 - Conference Proceeding

AN - SCOPUS:85114208551

T3 - Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

SP - 2127

EP - 2132

BT - Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

Y2 - 24 May 2021 through 27 May 2021

ER -

Hou D, Ma J, Huang S, Zhang J , Zhu X. Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning. In Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2127-2132. 9479376. (Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021). doi: 10.1109/ECCE-Asia49820.2021.9479376

Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning

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