Automatic Detection and Analysis of Photovoltaic Modules in Aerial Infrared Imagery
Abstract
Drone-based aerial thermography has become a convenient quality assessment tool for the precise localization of defective modules and cells in large photovoltaic-power plants. However, manual evaluation of aerial infrared recordings can be extremely time-consuming. Therefore, we propose an approach for automatic detection and analysis of photovoltaic modules in aerial infrared images. Significant temperature abnormalities such as hot spots and hot areas can be identified using our processing pipeline. To identify such defects, we first detect the individual modules in infrared images, and then use statistical tests to detect the defective modules. A quantitative evaluation of the detection and analysis pipeline on real-world, infrared recordings shows the applicability of our approach.
Cite
Text
Dotenco et al. "Automatic Detection and Analysis of Photovoltaic Modules in Aerial Infrared Imagery." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016. doi:10.1109/WACV.2016.7477658Markdown
[Dotenco et al. "Automatic Detection and Analysis of Photovoltaic Modules in Aerial Infrared Imagery." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016.](https://mlanthology.org/wacv/2016/dotenco2016wacv-automatic/) doi:10.1109/WACV.2016.7477658BibTeX
@inproceedings{dotenco2016wacv-automatic,
title = {{Automatic Detection and Analysis of Photovoltaic Modules in Aerial Infrared Imagery}},
author = {Dotenco, Sergiu and Dalsass, Manuel and Winkler, Ludwig and Wurzner, Tobias and Brabec, Christoph and Maier, Andreas K. and Gallwitz, Florian},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2016},
pages = {1-9},
doi = {10.1109/WACV.2016.7477658},
url = {https://mlanthology.org/wacv/2016/dotenco2016wacv-automatic/}
}