single crystalline silicon
Recently Published Documents


TOTAL DOCUMENTS

482
(FIVE YEARS 40)

H-INDEX

40
(FIVE YEARS 3)

2021 ◽  
Vol 155 (20) ◽  
pp. 204202
Author(s):  
Chien-Hsuan Li ◽  
Yu-Lung Tang ◽  
Junichi Takahara ◽  
Shi-Wei Chu

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4292
Author(s):  
Horng-Horng Lin ◽  
Harshad Kumar Dandage ◽  
Keh-Moh Lin ◽  
You-Teh Lin ◽  
Yeou-Jiunn Chen

Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about 2.71 s. The average segmentation errors along the x-direction and y-direction are only 1.6 pixels and 1.4 pixels, respectively. The defect detection approach on segmented cells achieves 99.8% accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization.


2021 ◽  
Author(s):  
Erfan Pourshaban ◽  
Aishwaryadev Banerjee ◽  
Chayanjit Ghosh ◽  
Adwait Deshpande ◽  
Hanseup Kim ◽  
...  

2021 ◽  
pp. 2008171
Author(s):  
Bing‐Chang Zhang ◽  
Yi‐Hao Shi ◽  
Jie Mao ◽  
Si‐Yi Huang ◽  
Zhi‐Bin Shao ◽  
...  

Author(s):  
Michel Piliougine ◽  
Amal Oukaja ◽  
Paula Sánchez‐Friera ◽  
Giovanni Petrone ◽  
Francisco José Sánchez‐Pacheco ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document