面向太阳能电池复杂缺陷检测的新型多步深度学习方法

doi:10.1007/s12204-023-2670-3

摘要/Abstract

摘要： 太阳能电池片的缺陷变化大且种类多，部分缺陷样本难以获取或者尺度较小，存在小样本与小目标的问题，为太阳能电池片缺陷检测带来了挑战。为了解决这一问题，本文提出了一种多阶段的太阳能电池复杂缺陷检测方法。首先提取电致发光图像中的单块细胞板进行逐块检测，再通过StyleGAN2-ada进行GAN数据增强，扩充小样本缺陷数量，最后在混合数据集上通过改进后的YOLOv5进行训练。通过实验证明该方法得到的模型面对小样本、小目标的缺陷表现更加优秀，最后结果召回率达到了99.7%，相较于未改进前提高了3.9%，同时精确率与平均精度均值也分别提高了3.4%、3.5%。并且实验表明，使用混合数据集训练与改进网络的方式可以提高模型的检测效果，两者共用有效地提高了网络对于太阳能电池缺陷的检测性能。

关键词: 智能制造, 智能缺陷识别, 深度学习, 数据增强, 太阳能电池

Abstract: Solar cell defects exhibit significant variations and multiple types, with some defect data being difficult to acquire or having small scales, posing challenges in terms of small sample and small target in defect detection for solar cells. In order to address this issue, this paper proposes a multi-step approach for detecting the complex defects of solar cells. First, individual cell plates are extracted from electroluminescence images for block-by-block detection. Then, StyleGAN2-Ada is utilized for generative adversarial networks data augmentation to expand the number of defect samples in small sample defects. Finally, the fake dataset is combined with real dataset, and the improved YOLOv5 model is trained on this mixed dataset. Experimental results demonstrate that the proposed method achieves a superior performance in detecting the defects with small sample and small target, with the final recall rate reaching 99.7%, an increase of 3.9% compared with the unimproved model. Additionally, the precision and mean average precision are increased by 3.4% and 3.5%, respectively. Moreover, the experiments demonstrate that the improved network training on the mixed dataset can effectively enhance the detection performance of the model. The combination of these approaches significantly improves the network’s ability to detect solar cell defects.

中图分类号:

TP391.4

. 面向太阳能电池复杂缺陷检测的新型多步深度学习方法[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 1050-1064.

JIANG Wenbo, ZHENG Hangbin, BAO Jinsong. Novel Multi-Step Deep Learning Approach for Detection of Complex Defects in Solar Cells[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 1050-1064.

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