Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets
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Abstract (arXiv)
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.
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References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
- 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 997
- 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 514
- 2022SolarDK: A high-resolution urban solar panel image classification and localization datasetcited by 3
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