论文标题
用于薄膜光伏模块的电致发光图像的编码器语义分割模型
Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules
论文作者
论文摘要
我们考虑了一系列基于深神经网络的图像分割方法，以便对薄膜模块的电致发光（EL）图像进行语义分割。我们利用编码器深度神经网络体系结构。该框架是一般的，因此可以轻松地将其扩展到其他类型的图像（例如热量表）或太阳能电池技术（例如晶体硅模块）。对网络进行了训练和测试，并在数据库中的图像样本中进行了6000张EL图像的图像，并将其图像铜imp铜（CIGS）薄膜模块。我们选择了两种类型的功能来提取，分流和所谓的“液滴”。后一个特征通常在图像集中观察到。使用编码器层的各种组合对几种模型进行了测试，并提出了一个过程来选择最佳模型。我们通过最佳选择的模型显示了模范结果。此外，我们将最佳模型应用于全套6000张图像，并证明EL图像的自动分割可以揭示许多微妙的特征，这些特征无法通过研究一小部分图像来推断出来。我们认为这些功能可以有助于过程优化和质量控制。
We consider a series of image segmentation methods based on the deep neural networks in order to perform semantic segmentation of electroluminescence (EL) images of thin-film modules. We utilize the encoder-decoder deep neural network architecture. The framework is general such that it can easily be extended to other types of images (e.g. thermography) or solar cell technologies (e.g. crystalline silicon modules). The networks are trained and tested on a sample of images from a database with 6000 EL images of Copper Indium Gallium Diselenide (CIGS) thin film modules. We selected two types of features to extract, shunts and so called "droplets". The latter feature is often observed in the set of images. Several models are tested using various combinations of encoder-decoder layers, and a procedure is proposed to select the best model. We show exemplary results with the best selected model. Furthermore, we applied the best model to the full set of 6000 images and demonstrate that the automated segmentation of EL images can reveal many subtle features which cannot be inferred from studying a small sample of images. We believe these features can contribute to process optimization and quality control.
