تقرير
Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets
| العنوان: | Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets |
|---|---|
| المؤلفون: | Sagaram, Sankarshanaa, Didwania, Krish, Srivastava, Laven, Kasliwal, Aditya, Kailas, Pallavi, Verma, Ujjwal |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: Published at ICLR Tiny Paper 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.12843 |
| رقم الأكسشن: | edsarx.2402.12843 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |