Deep Learning Inspection for Photovoltaic Cell Image Sequence

doi:10.3901/JME.2021.08.098

Abstract

Abstract: In order to realize intelligent factory inspection of photovoltaic cell, the defects of photovoltaic cell are detected by using thermal infrared camera acquiring electro-thermography(ET) and short-wave infrared camera acquiring electroluminescence(EL). A thermal image sequence analysis method based on optical flow method to deal with the thermal flow field of photovoltaic cells is proposed to find the abnormal heat source accurately. The database of photovoltaic cell detection is established by fusing the abnormal light source found by short-wave infrared imaging. The deep convolution neural network is used to effectively identify the artificial defects and internal defects of photovoltaic cells, such as scratches, covers, cracks and defects. The experimental results show that the deep learning method based on optical flow is superior to principal component analysis(PCA) and independent component analysis(ICA) in mean square error, average gradient and information entropy index, and make the network converge faster in the training of convolution network.

Key words: photovoltaic cell, non-destructive testing, image fusion, deep learning

CLC Number:

TM914

DENG Baoyuan, HE Yunze, WANG Hongjin, ZHANG Hong, YANG Yuan, MA Minmin, MU Xinying, YANG Ruizhen. Deep Learning Inspection for Photovoltaic Cell Image Sequence[J]. Journal of Mechanical Engineering, 2021, 57(8): 98-106.

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