Solar Panel Defect Classification & Re-Training Pipeline: Revolutionizing Maintenance for the Solar Energy Industry
Key Highlights
- Explore how machine learning and image processing technologies transform solar panel inspections, improving accuracy and speed.
- Learn how continual re-training with new data keeps defect detection adaptive and relevant as conditions change.
- Discover how techniques like ESRGAN enhance low-resolution images, leading to better detection of cracks, hotspots, and other defects.
- See how the pipeline leverages AWS and Databricks for scalable, efficient maintenance solutions across large solar installations.
- Understand how real-time monitoring and predictive maintenance help reduce downtime, extend panel lifespan, and lower operational costs.