Document Type
Article
Publication Date
9-23-2025
Department
Department of Applied Computing; Department of Manufacturing and Mechanical Engineering Technology; Department of Mechanical and Aerospace Engineering
Abstract
Snow accumulation on photovoltaic (PV) panels can cause significant energy losses in cold climates. While drone-based monitoring offers a scalable solution, real-world challenges like varying illumination can hinder accurate snow detection. We previously developed a YOLO-based drone system for snow coverage detection using a Fixed Thresholding segmentation method to discriminate snow from the solar panel; however, it struggled in challenging lighting conditions. This work addresses those limitations by presenting a reliable drone-based system to accurately estimate the Snow Coverage Percentage (SCP) over PV panels. The system combines a lightweight YOLOv11n-seg deep learning model for panel detection with an adaptive image processing algorithm for snow segmentation. We benchmarked several segmentation models, including MASK R-CNN and the state-of-the-art SAM2 segmentation model. YOLOv11n-seg was selected for its optimal balance of speed and accuracy, achieving 0.99 precision and 0.80 recall. To overcome the unreliability of static thresholding under changing lighting, various dynamic methods were evaluated. Otsu’s algorithm proved most effective, reducing the absolute error of the mean in SCP estimation to just 1.1%, a significant improvement over the 13.78% error from the previous fixed-thresholding approach. The integrated system was successfully validated for real-time performance on live drone video streams, demonstrating a highly accurate and scalable solution for autonomous snow monitoring on PV systems.
Publication Title
Energies
Recommended Citation
Mazen, A.,
Saleem, A.,
Yazdipaz, K.,
&
Dyreson, A.
(2025).
Overcoming Variable Illumination in Photovoltaic Snow Monitoring: A Real-Time Robust Drone-Based Deep Learning Approach.
Energies,
18(19).
http://doi.org/10.3390/en18195092
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2067
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Included in
Aerospace Engineering Commons, Computer Sciences Commons, Materials Science and Engineering Commons, Mechanical Engineering Commons
Publisher's Statement
Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. Publisher’s version of record: https://doi.org/10.3390/en18195092