Defects detection method of photovoltaic cells based on lightweightconvolutional neural network

LIU Huaiguang; DING Wancheng; HUANG Qianwen

doi:10.5768/JAO202243.0103003

Journal of Applied Optics > 2022 > 43(1): 87-94. > DOI: 10.5768/JAO202243.0103003

LIU Huaiguang, DING Wancheng, HUANG Qianwen. Defects detection method of photovoltaic cells based on lightweightconvolutional neural network[J]. Journal of Applied Optics, 2022, 43(1): 87-94. DOI: 10.5768/JAO202243.0103003

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Defects detection method of photovoltaic cells based on lightweightconvolutional neural network

1.
Key Laboratory of Metallurgical Equipment and Control Technology (Ministry of Education), 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: August 17, 2021
Revised Date: September 06, 2021
Available Online: September 16, 2021

Graphical Abstract

Abstract

Abstract

The defects in photovoltaic cells affect the service life and power generation efficiency of the entire photovoltaic system. Aiming at the high missed detection rate of weak and small defects in the automatic detection of existing cells, a feature-enhanced lightweight convolutional neural network model was established. The feature enhancement extraction module was designed specifically to improve the extraction ability of weak boundaries. In addition, according to the principle of multi-scale recognition, a small target prediction layer was added to realize multi-scale feature prediction. In the experimental test, the mean average precision (mAP) of the model reaches to 87.55%, which is 6.78 percentage points higher than the traditional model. Moreover, the detection speed reaches to 40 fps, which meets the accuracy and real-time detection requirements.
- photovoltaic cells,
- defects detection,
- deep learning,
- feature extraction,
- small target prediction

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References (15)

References

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Defects detection method of photovoltaic cells based on lightweightconvolutional neural network