论文标题
使用标准化的LP规范对太阳能电池的裂缝进行弱监督分割
Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm
论文作者
论文摘要
Photovoltaic是最重要的可再生能源之一，用于处理全球范围内的能源消耗。这增加了对生产和运营期间快速可扩展的自动质量管理的需求。但是，单或多晶太阳能模块的电致发光（EL）图像上裂纹的检测和分割是一项艰巨的任务。在这项工作中，我们提出了一种弱监督的学习策略，该策略仅使用图像级注释来获得能够分割太阳能电池图像的裂纹的方法。我们使用修改后的Resnet-50来从网络激活图中得出分割。我们使用缺陷分类作为替代任务来训练网络。为此，我们将归一化的LP归一化应用于将激活图汇总为单个分数进行分类。此外，我们还提供了一项研究，归一化LP层的不同参数如何影响分割性能。这种方法显示了给定任务的有希望的结果。但是，我们认为该方法也有可能解决其他弱监督的细分问题。
Photovoltaic is one of the most important renewable energy sources for dealing with world-wide steadily increasing energy consumption. This raises the demand for fast and scalable automatic quality management during production and operation. However, the detection and segmentation of cracks on electroluminescence (EL) images of mono- or polycrystalline solar modules is a challenging task. In this work, we propose a weakly supervised learning strategy that only uses image-level annotations to obtain a method that is capable of segmenting cracks on EL images of solar cells. We use a modified ResNet-50 to derive a segmentation from network activation maps. We use defect classification as a surrogate task to train the network. To this end, we apply normalized Lp normalization to aggregate the activation maps into single scores for classification. In addition, we provide a study how different parameterizations of the normalized Lp layer affect the segmentation performance. This approach shows promising results for the given task. However, we think that the method has the potential to solve other weakly supervised segmentation problems as well.
