Name
Title | ||
|---|---|---|
Convolution neural network based polycrystalline silicon photovoltaic cell linear defect diagnosis using electroluminescence images |
Abstract | ||
|---|---|---|
•A three-phase algorithm is proposed for automatic linear defects diagnosis is proposed.•The solution combined the advantages of the traditional image processing techique and deep learning.•The solution obtain the best trade-off between computing accuracy and complexity.•A dataset of PV module EL images is well established and maintained. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.eswa.2022.117087 | Expert Systems with Applications |
Keywords | DocType | Volume |
Electroluminescence images,Defects classification,Feature extraction,Deep learning | Journal | 202 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wuqin Tang | 1 | 0 | 0.34 |
| Qiang Yang | 2 | 0 | 0.34 |
| Xiaochen Hu | 3 | 0 | 0.34 |
| W. J. Yan | 4 | 1 | 3.43 |