YOLO-DSBF： A NOVEL METHOD FOR SOLAR CELL DEFECT DETECTION

He Yijie; Chu Ying; Xia Nenghong; Jiang Zhengyuan; Li Xi

doi:10.19912/j.0254-0096.tynxb.2024-1226

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 280-288. DOI: 10.19912/j.0254-0096.tynxb.2024-1226

YOLO-DSBF： A NOVEL METHOD FOR SOLAR CELL DEFECT DETECTION

He Yijie, Chu Ying, Xia Nenghong, Jiang Zhengyuan, Li Xi

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Abstract

Addressing the prevalent issues of low recognition accuracy and inaccurate detection in current solar cell defect detection models, this paper introduces a novel method, YOLO-DSBF, for solar cell defect detection. Firstly, the C2f layer in the YOLOv8 head network is integrated with Dynamic Snake Convolution （DSConv） to create the C2f_DSConv module, which enables adaptive focusing on elongated local defects. Secondly, a dynamic sparse attention mechanism （BiFormer） is incorporated into the neck network, facilitating more flexible computation allocation and content awareness, thereby enhancing the model's feature extraction capabilities. Subsequently, a small object detection layer is introduced to target micro-defects, effectively mitigating the missed detection rate. Lastly, replacing the original CIoU loss function with the GIoU loss function effectively enhances the regression performance of the algorithm. Experimental results reveal that, in comparison to the baseline model YOLOv8n, the proposed model exhibits improvements of 5.64%, 5%, and 13.29% in mAP@0.5, mAP@0.5：0.95, and precision, respectively. Without increasing the model size, this model surpasses other detection models in detection accuracy, rendering it more appropriate for solar cell defect detection tasks.

Key words

solar cells / object detection / deep learning / YOLOv8 / BiFormer / GIoU

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He Yijie, Chu Ying, Xia Nenghong, Jiang Zhengyuan, Li Xi. YOLO-DSBF： A NOVEL METHOD FOR SOLAR CELL DEFECT DETECTION[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 280-288 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1226

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