solar_panel_combine Object Detection Dataset and Pre-Trained Model by home

Images

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Explore Dataset

Here are a few use cases for this project:

Solar Panel Maintenance: The model could be used by solar panel service providers to automate the process of assessment and maintenance. By analyzing the state of the panels (clean, unclean, or dusty) it can help them identify which panels need immediate cleaning or service.
Industrial Inspection: In facilities with a large number of solar panels such as solar farms, the model could assist in streamlining routine checks. Rather than manual inspection, images can be taken and analyzed for cleanliness, helping to efficiently allocate cleaning resources and maintain optimum efficiency.
Home Automation Systems: The model could be integrated into smart home systems to alert homeowners when their solar panels are dirty or dusty. It can act as a smart tool for homes using solar energy as one of their primary energy sources.
Drone-based Inspection: For large scale solar installations in hard-to-reach areas (e.g. large roofs, deserts), drones equipped with cameras and the computer vision model can perform inspections. This can be safer and more effective, with the AI determining the status of each panel.
Educational Purposes: This computer vision model could be used as a teaching tool in educational institutions for courses related to renewable energy. It can demonstrate the importance of solar panel cleanliness in energy efficiency, encouraging students to engage with practical, real-world issues in their learning.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            solar_panel_combine_dataset,
                            title = { solar_panel_combine Dataset },
                            type = { Open Source Dataset },
                            author = { home },
                            howpublished = { \url{ https://universe.roboflow.com/home-ocdun/solar_panel_combine } },
                            url = { https://universe.roboflow.com/home-ocdun/solar_panel_combine },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { sep },
                            note = { visited on 2024-05-13 },
                            }