# Computer Vision Tool for Detection, Mapping and Fault Classification of PV Modules in Aerial IR Videos

arXiv: https://arxiv.org/abs/2106.07314 · HTML: https://mlsanity.com/paper/2106.07314/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)

## Built on by (most cited first, 6 of 6)

- [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)
- [Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning](https://mlsanity.com/paper/2112.02922/) (2021)
- [Georeferencing of Photovoltaic Modules from Aerial Infrared Videos using Structure-from-Motion](https://mlsanity.com/paper/2204.02733/) (2022)
- [Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up](https://mlsanity.com/paper/2110.03923/) (2021)
- [Machine learning approaches for automatic defect detection in photovoltaic systems](https://mlsanity.com/paper/2409.16069/) (2024)
- [Method of UAV Inspection of Photovoltaic Modules Using Thermal and RGB Data Fusion](https://mlsanity.com/paper/2512.06504/) (2025)

## Abstract (arXiv)

Increasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In this work, we develop a computer vision tool for the semi-automatic extraction of PV modules from thermographic UAV videos. We use it to curate a dataset containing 4.3 million IR images of 107842 PV modules from thermographic videos of seven different PV plants. To demonstrate its use for automated PV plant inspection, we train a ResNet-50 to classify ten common module anomalies with more than 90 % test accuracy. Experiments show that our tool generalizes well to different PV plants. It successfully extracts PV modules from 512 out of 561 plant rows. Failures are mostly due to an inappropriate UAV trajectory and erroneous module segmentation. Including all manual steps our tool enables inspection of 3.5 MW p to 9 MW p of PV installations per day, potentially scaling to multi-gigawatt plants due to its parallel nature. While we present an effective method for automated PV plant inspection, we are also confident that our approach helps to meet the growing demand for large thermographic datasets for machine learning tasks, such as power prediction or unsupervised defect identification.