Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916270465613824 |
|---|---|
| author | Sagaram, Sankarshanaa Didwania, Krish Srivastava, Laven Kasliwal, Aditya Kailas, Pallavi Verma, Ujjwal |
| author_facet | Sagaram, Sankarshanaa Didwania, Krish Srivastava, Laven Kasliwal, Aditya Kailas, Pallavi Verma, Ujjwal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12843 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets Sagaram, Sankarshanaa Didwania, Krish Srivastava, Laven Kasliwal, Aditya Kailas, Pallavi Verma, Ujjwal Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2402.12843 |