Intelligent Defect Detection Method of Photovoltaic Modules Based on Deep Learning

DOI

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

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
BT  - Proceedings of the 2018 International Conference on Transportation & Logistics, Information & Communication, Smart City (TLICSC 2018)
PB  - Atlantis Press
SP  - 167
EP  - 173
SN  - 1951-6851
UR  - https://doi.org/10.2991/tlicsc-18.2018.27
DO  - 10.2991/tlicsc-18.2018.27
ID  - Ni2018/12
ER  -