Proceedings of the 2018 International Conference on Transportation & Logistics, Information & Communication, Smart City (TLICSC 2018)

Intelligent Defect Detection Method of Photovoltaic Modules Based on Deep Learning

Authors
Binbin Ni, Pingguo Zou, Qiang Li, Yabin Chen
Corresponding Author
Binbin Ni
Available Online December 2018.
DOI
https://doi.org/10.2991/tlicsc-18.2018.27How to use a DOI?
Keywords
Deep learning; Neural network; Photovoltaic modules; Defect detection.
Abstract
Currently, photovoltaic module manufacturers still rely on manual detection of EL images of photovoltaic modules to identify hidden defects. EL image detection is an important link in the quality control of photovoltaic modules production. Manual detection leads to slow detection speed, and the accuracy is affected by personal subjective judgment. In this paper, an intelligent defect detection method based on deep learning is proposed. The method first builds a network according to the sample characteristics. The initial network value is obtained through training. Then, the neural algorithm is used to adjust the network parameters to obtain the mapping relationship between training samples and defect-free templates. Finally, the comparison between reconstructed image and defect image is used to realize defect detection of test samples. Experiments show that the method based on deep learning can detect defects accurately and quickly.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2018
ISBN
978-94-6252-621-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/tlicsc-18.2018.27How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Binbin Ni
AU  - Pingguo Zou
AU  - Qiang Li
AU  - Yabin Chen
PY  - 2018/12
DA  - 2018/12
TI  - Intelligent Defect Detection Method of Photovoltaic Modules Based on Deep Learning
PB  - Atlantis Press
SP  - 167
EP  - 173
SN  - 1951-6851
UR  - https://doi.org/10.2991/tlicsc-18.2018.27
DO  - https://doi.org/10.2991/tlicsc-18.2018.27
ID  - Ni2018/12
ER  -