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

TL;DR

This paper presents a computer vision tool that automates the extraction and classification of PV modules from UAV thermographic videos, enabling scalable inspection and dataset creation for large PV plants.

Contribution

The development of a semi-automatic tool for PV module detection and anomaly classification from UAV IR videos, facilitating large-scale PV plant inspection and dataset generation.

Findings

Successfully extracted PV modules from 512 out of 561 plant rows.

Achieved over 90% accuracy in classifying ten common module anomalies.

Enabled inspection of 3.5 to 9 MWp of PV installations per day.

Abstract

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.