The increasing adoption of solar energy necessitates advanced methodologies for monitoring and maintenance to ensure optimal performance of solar panel installations. A critical component in this context is the accurate segmentation of solar panels from aerial or satellite imagery, which is essential for identifying operational issues and assessing efficiency. This paper addresses the significant challenges in panel segmentation, particularly the scarcity of annotated data and the labour-intensive nature of manual annotation for supervised learning. We explore and apply Self-Supervised Learning (SSL) to solve these challenges. We demonstrate that SSL significantly enhances model generalization under various conditions and reduces dependency on manually annotated data, paving the way for robust and adaptable solar panel segmentation solutions.

翻译：随着太阳能应用的日益普及，亟需先进的监测与维护方法来确保太阳能面板设施的最优运行性能。在此背景下，从航空或卫星影像中精确分割太阳能面板成为关键环节，这对识别运行故障和评估发电效率至关重要。本文针对面板分割中存在的重大挑战展开研究，特别是标注数据稀缺以及监督学习所需人工标注的费时费力问题。我们探索并应用自监督学习方法来解决这些挑战。实验证明，自监督学习能显著提升模型在多种条件下的泛化能力，并降低对人工标注数据的依赖，为构建鲁棒且适应性强的太阳能面板分割方案奠定了基础。