YOLOv5 IS USED IN OPTIMIZATION OF SURFACE DEFECT DETECTION OF SOLAR CELLS

Mai Yaohua; Wu Shaohang; Gao Yanyan; Li Yang; Li Yujuan; Zhou Jielong

doi:10.19912/j.0254-0096.tynxb.2023-1027

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 162-169. DOI: 10.19912/j.0254-0096.tynxb.2023-1027

YOLOv5 IS USED IN OPTIMIZATION OF SURFACE DEFECT DETECTION OF SOLAR CELLS

Li Yujuan^1,2, Zhou Jielong³, Mai Yaohua^2,4, Wu Shaohang^2,4, Gao Yanyan^2,4, Li Yang¹

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Abstract

In the field of new energy application technology, surface defect detection of solar cells is a crucial technical component. An optimized model based on the YOLOv5 algorithm is researched and proposed, which can detect static images and be used in real-time video. The model incorporates coordinate attention （CA） into the backbone network of YOLOv5 and optimizes the feature fusion part of the neck network using BiFPN structure. Furthermore, to address the issue of imbalanced positive and negative samples, Focal Loss is employed and optimized to Varifocal Loss. This optimized algorithm is applied to the PVEL-AD dataset, with mAP@0.5 used as the validation metric. The validation results demonstrate that the algorithm achieves a detection accuracy of 86.24%, which represents a 5.64% improvement compared to the unoptimized original algorithm.

Key words

object detection / convolutional neural networks / deep learning / solar cells / YOLOv5

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Li Yujuan, Zhou Jielong, Mai Yaohua, Wu Shaohang, Gao Yanyan, Li Yang. YOLOv5 IS USED IN OPTIMIZATION OF SURFACE DEFECT DETECTION OF SOLAR CELLS[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 162-169 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1027

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