SOLAR CELL SURFACE DEFECT DETECTION BASED ON MULTI-SCALE FEATURE FUSION

Liu Tao; Sun Hui; Bian Baicheng

doi:10.19912/j.0254-0096.tynxb.2023-1689

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 342-349. DOI: 10.19912/j.0254-0096.tynxb.2023-1689

SOLAR CELL SURFACE DEFECT DETECTION BASED ON MULTI-SCALE FEATURE FUSION

Liu Tao, Sun Hui, Bian Baicheng

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Abstract

Detection difficulty caused by multi-source and miscellaneous defects and micro-size defects on the surface of polycrystalline silicon solar cells has bothered photovoltaic industry for a long time. Surface defect detection algorithm driven by machine learning is struggling in the troubles of low-accuracy, less-feature and bad robustness during industrial running. Based on deep learning, a novel lightweight defect detection model with multi-scale feature fusion is put forward to narrow the error gap. Framed by YOLOv5 （the object detection algorithm）, for the goal of enhancing the multi-scale feature fusion effect, shallow channel fusion branch and dual sampling structure are employed, as well the multi-scale pooling pyramid is modified, to improve the detection accuracy of micro-size defects. In this framework, efficient channel attention mechanism is introduced to enhance the neck network’s attention, a new downsampling structure and C3 （Light-CSP-Darknet53） structure are added to increase the accuracy and decrease computation. Experimental results demonstrate that this modified model can increase average accuracy （mAP_0.5） by 8.5%, reduce the number of parameters by 44.3% and decrease computation complexity by 33.7%.

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solar cells / surface defect / object detection / attention mechanism / multi-scale feature fusion

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Liu Tao, Sun Hui, Bian Baicheng. SOLAR CELL SURFACE DEFECT DETECTION BASED ON MULTI-SCALE FEATURE FUSION[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 342-349 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1689

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