RESEARCH ON DEFECT DETECTION OF SOLAR CELLS WITH YOLOv8 ALGORITHM

Wang Zongliang; Lu Li; Kang Xiaodong; Ruan Xiaopeng; Da Xianwen

doi:10.19912/j.0254-0096.tynxb.2024-0572

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 379-386. DOI: 10.19912/j.0254-0096.tynxb.2024-0572

RESEARCH ON DEFECT DETECTION OF SOLAR CELLS WITH YOLOv8 ALGORITHM

Wang Zongliang, Lu Li, Kang Xiaodong, Ruan Xiaopeng, Da Xianwen

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Abstract

Aiming at the problems of defect scale inconsistency and serious interference of background noise in the defect detection process of solar cells under electroluminescence conditions, a defect detection algorithm based on YOLOv8 is proposed to accurately identify the defects on the surface of the cells. Firstly, the sensing field attention convolution is used to make the model dynamically adjust the sensing field weights, reduce the blurring and omission of image information, and improve the detection effect of tiny defects. Secondly, the coordinate attention mechanism is introduced to obtain the aspect direction information, highlight the key features, and alleviate the interference of the background noise; finally, the SIoU is used as the bounding box loss function, and the vector angle is incorporated into the consideration criterion in the process of predicting the convergence of the box. The experimental results show that the algorithm achieves a mAP of 92.83% on the solar cell defect detection dataset, which is an improvement of 2.1% compared with the original network, and the cases of misdetection and omission are significantly reduced, which is able to effectively locate and identify the defects on solar cells.

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deep learning / solar cells / electroluminescence / convolutional neural networks / object detection

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Wang Zongliang, Lu Li, Kang Xiaodong, Ruan Xiaopeng, Da Xianwen. RESEARCH ON DEFECT DETECTION OF SOLAR CELLS WITH YOLOv8 ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 379-386 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0572

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