# Automatic Inspection of Utility Scale Solar Power Plants using Deep Learning

arXiv: https://arxiv.org/abs/1902.04132 · HTML: https://mlsanity.com/paper/1902.04132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle](https://mlsanity.com/paper/2111.11709/) (2021)

## Abstract (arXiv)

Solar energy has the potential to become the backbone energy source for the world. Utility scale solar power plants (more than 50 MW) could have more than 100K individual solar modules and be spread over more than 200 acres of land. Traditionally methods of monitoring each module become too costly in the utility scale. We demonstrate an alternative using the recent advances in deep learning to automatically analyze drone footage. We show that this can be a quick and reliable alternative. We show that it can save huge amounts of power and the impact the developing world hugely.