SSN: Shift Suppression Network for Endogenous Shift of Photovoltaic Defect Detection

Different from traditional defect detection methods, our method is a domain generalization model.

Electroluminescence (EL) Endogenous Shift (ELES) dataset is the first endogenous domain shift benchmark of photovoltaic manufacturing scenarios. Due to the differences in intrinsic mechanisms or hardware components of the imaging systems of different types of imaging conditions, there are subtle perturbations in EL images generated by different quality inspection production line, which cause the endogenous shift.

2023 Dataset Access Instructions:

We collected three sets of EL data (EL group 1, EL group 2, and EL group 3) at different times during the production line update process of the same manufacturer. In this study, EL group 1 is used for training the detector, and the other two are used for the test. To efficiently test PV modules, we slice a PV module based on the unit of 2 cells and spliced it into a complete module after the detection. Combined with deep learning technology and with the assistance of multiple experts, we meticulously annotate all defects. The dataset contains a total of $16,323$ EL PV images.

Number of defects	EL group 1	EL group 2	EL group 3	Total
broken gate	2436	909	335	1620
unjoined weld	1563	1167	275	1669
black spot	2313	833	270	1440
crack	1951	187	62	508
scratch	2219	1011	257	1567

Datasets	EL group 1	EL group 2	EL group 3
Number of defective image	7762	3209	839
Number of defect-free image	1410	2541	562
Total	9172	5750	1401
Acquisition time	September 2020	May 2021	October 2021
Resolution	384 x 384	640 x 589	700 x 668

Download Dataset

If you want to access the dataset, please read the ELES-Dataset Request Form in this folder carefully, sign the corresponding commitment file, and send it to [email protected].

EDS dataset consists of:

• 3 different domains

• Altogether 16,323 EL images

Citations

If you are using the code/model/data provided here in a publication, please consider citing:

@ARTICLE{3327572,
author={Zhao, Shenshen, and Chen, Haiyong, and Wang, Chuhan, and Zhong, Zhang},
journal={IEEE Transactions on Industrial Informatics}, 
title={SSN: Shift Suppression Network for Endogenous Shift of Photovoltaic Defect Detection}, 
year={2023},
volume={},
number={},
pages={1-13},
doi={10.1109/TII.2023.3327572}
}