Research on detection agorithm of solar cell component defects based on deep neural network

LIU Huaiguang; LIU Anyi; ZHOU Shiyang; LIU Hengyu; YANG Jintang

doi:10.5768/JAO202041.0202006

Journal of Applied Optics > 2020 > 41(2): 327-336. > DOI: 10.5768/JAO202041.0202006

LIU Huaiguang, LIU Anyi, ZHOU Shiyang, LIU Hengyu, YANG Jintang. Research on detection agorithm of solar cell component defects based on deep neural network[J]. Journal of Applied Optics, 2020, 41(2): 327-336. DOI: 10.5768/JAO202041.0202006

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Research on detection agorithm of solar cell component defects based on deep neural network

1.
Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China
2.
Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China

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Received Date: September 22, 2019
Revised Date: November 15, 2019
Available Online: March 31, 2020

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Abstract

Abstract

Aiming at the problem that the cracked cell in the solar cell module eventually causes the whole cell to break and affect the power generation of the whole component, a method for detecting cracked defects of battery components using convolutional neural network network (CNN), is proposed based on the screening and positioning of the photoluminescence (PL) image of the battery component. The basic idea is to obtain the image of the battery component by using the PL detection technology first, then pre-process the image, filter and locate the target area based on the clustering method, and finally use three convolutional neural network models to detect the defect of the battery, and compare the accuracy. A large number of experimental results verify that the above method can accurately detect the cracking defects of solar cell modules.
- photoluminescence,
- convolutional neural network,
- image recognition,
- defects detection

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