Abstract
Photovoltaic (PV) systems allow us to tap into all abundant solar energy, however they require regular maintenance for high efficiency and to prevent degradation. Traditional manual health check, using Electroluminescence (EL) imaging, is expensive and logistically challenging making automated defect detection essential. Current automation approaches require extensive manual expert labeling, which is time-consuming, expensive, and prone to errors. We propose PV-S3 (Photovoltaic-Semi Supervised Segmentation), a Semi-Supervised Learning approach for semantic segmentation of defects in EL images that reduces reliance on extensive labeling. PV-S3 is a Deep learning model trained using a few labeled images along with numerous unlabeled images. We introduce a novel Semi Cross-Entropy loss function to train PV-S3 which addresses the challenges specific to automated PV defect detection, such as diverse defect types and class imbalance. We evaluate PV-S3 on multiple datasets and demonstrate its effectiveness and adaptability. With merely 20% labeled samples, we achieve an absolute improvement of 9.7% in IoU, 29.9% in Precision, 12.75% in Recall, and 20.42% in F1-Score over prior state-of-the-art supervised method (which uses 100% labeled samples) on UCF-EL dataset (largest dataset available for semantic segmentation of EL images) showing improvement in performance while reducing the annotation costs by 80%.
Abstract (translated)
光伏（PV）系统允许我们利用丰富的太阳能能量，然而它们需要定期维护以实现高效和防止降解。传统的手动健康检查使用发光二极管（EL）成像，代价昂贵且具有挑战性，因此自动缺陷检测变得至关重要。目前的自动化方法需要大量手动专家标注，这需要花费时间、金钱，并且容易出错。我们提出了PV-S3（光伏-半监督分割），一种用于EL图像中缺陷语义分割的半监督学习方法，减少了对于广泛标注的依赖。 PV-S3是一个通过几张带标签图像和大量未标记图像进行训练的深度学习模型。我们引入了一种新颖的半交叉熵损失函数来训练PV-S3，解决了自动PV缺陷检测中特定的挑战，例如多样缺陷类型和类别不平衡。我们在多个数据集上评估PV-S3，并证明了其有效性和可适应性。 只需20%的带标签样本，我们实现了IoU绝对值 improve 9.7%，Precision绝对值 improve 29.9%，Recall绝对值 improve 12.75%，F1-Score绝对值 improve 20.42%，在UCF-EL数据集（可用于EL图像 semantic分割的最大数据集）上的性能改善，同时将 annotations costs 降低80%。
URL
https://arxiv.org/abs/2404.13693
