Detection method of solar cell surface defects based on C4.5-G algorithm

Shan Jin; YiLing Liu; ChenYun He

doi:10.1117/12.2678876

25 May 2023 Detection method of solar cell surface defects based on C4.5-G algorithm

Shan Jin, YiLing Liu, ChenYun He

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127120Q (2023) https://doi.org/10.1117/12.2678876
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

It is a very important method in nondestructive testing to detect defects using infrared or electroluminescent (EL) images of solar cell modules. Traditionally, this work is completed by skilled technicians, which is not only time-consuming but also vulnerable to subjective factors. The surface defect detection method of solar cells based on machine learning has become one of the main research directions for its high efficiency and convenience. Therefore, this paper proposes a defect detection method based on machine learning for solar cell EL image defect detection. First, the EL image is preprocessed to provide a clearer image with more obvious defects for subsequent feature extraction; Then, the gray, contrast, brightness and edge gradient features of the preprocessed image are extracted, and the edge gradient information of the defect is extracted using the improved LBP algorithm. Combined with the gray value, contrast and brightness information of the image, the training and testing data set of the classifier model is formed. Finally, the optimized decision tree algorithm - C4.5-G algorithm is used to classify the surface defects of the battery chip. While improving the performance of the algorithm, the convergence speed is accelerated and the over fitting phenomenon is avoided. The experimental results show that the method is feasible, effective and high detection rate in the defect detection of solar cells.

Citation Download Citation

Shan Jin, YiLing Liu, and ChenYun He "Detection method of solar cell surface defects based on C4.5-G algorithm", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127120Q (25 May 2023); https://doi.org/10.1117/12.2678876

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Solar cells

Decision trees

Education and training

Detection and tracking algorithms

Electroluminescence

Feature extraction

Defect detection

Show All Keywords

Keywords/Phrases

Search In:

Publication Years