Overcoming Variable Illumination in Photovoltaic Snow Monitoring: A Real-Time Robust Drone-Based Deep Learning Approach
Amna Mazen,
Ashraf Saleem (),
Kamyab Yazdipaz and
Ana Dyreson
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Amna Mazen: Department of Applied Computing, College of Computing, Michigan Technological University, Houghton, MI 49931, USA
Ashraf Saleem: Department of Applied Computing, College of Computing, Michigan Technological University, Houghton, MI 49931, USA
Kamyab Yazdipaz: Department of Applied Computing, College of Computing, Michigan Technological University, Houghton, MI 49931, USA
Ana Dyreson: Department of Mechanical and Aerospace Engineering, College of Engineering, Michigan Technological University, Houghton, MI 49931, USA
Energies, 2025, vol. 18, issue 19, 1-15
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.
Keywords: photovoltaic (PV) systems; snow detection; drone imagery; dynamic thresholding; Otsu’s method; snow coverage percentage (SCP) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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