论文标题
用于缺陷的异常分割模型可在异质结太阳能电池的电致发光图像中检测
Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells
论文作者
论文摘要
太阳能电池制造中的有效缺陷检测对于稳定的绿色能源技术制造至关重要。本文提出了一种基于深度学习的自动检测模型SEMACNN，用于分类和语义分割电致发光图像，用于太阳能电池质量评估和异常检测。该模型的核心是一种基于马哈拉氏症距离的异常检测算法，该算法可以在不平衡的数据上以少量具有相关缺陷的数字电致发光图像进行半监督的方式进行训练。这对于迅速将模型集成到工业格局中特别有价值。该模型已通过植物收集的数据集进行了训练，该数据集由68 748个带有母线网格的异质结太阳能电池的电发光图像组成。我们的模型达到了92.5％，F1得分95.8％，召回94.8％和精度为96.9％的精度，该验证子集由1049个手动注释的图像组成。该模型还在Open ELPV数据集上进行了测试，并证明了稳定的性能，准确性为94.6％，F1得分为91.1％。 SEMACNN模型展示了其性能和计算成本之间的良好平衡，这使其适用于集成到太阳能电池制造的质量控制系统中。
Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and semantic segmentation of electroluminescent images for solar cell quality evaluation and anomalies detection. The core of the model is an anomaly detection algorithm based on Mahalanobis distance that can be trained in a semi-supervised manner on imbalanced data with small number of digital electroluminescence images with relevant defects. This is particularly valuable for prompt model integration into the industrial landscape. The model has been trained with the on-plant collected dataset consisting of 68 748 electroluminescent images of heterojunction solar cells with a busbar grid. Our model achieves the accuracy of 92.5%, F1 score 95.8%, recall 94.8%, and precision 96.9% within the validation subset consisting of 1049 manually annotated images. The model was also tested on the open ELPV dataset and demonstrates stable performance with accuracy 94.6% and F1 score 91.1%. The SeMaCNN model demonstrates a good balance between its performance and computational costs, which make it applicable for integrating into quality control systems of solar cell manufacturing.
