YOLO algorithms for Solar Panel and Boiler Detection from Aerial Imagery
Keywords:
YOLO, Solar Panel Detection, Boiler Detection, Deep Learning, Drone Imagery, Satellite ImagerySynopsis
This is a Chapter in:
Book:
Intelligent and Sustainable Solutions
Print ISBN 978-1-6692-0012-3
Online ISBN 978-1-6692-0011-6
Series:
Chronicle of Computing
Chapter Abstract:
The integration of solar panels and boilers, considered key renewable energy technologies, remains crucial for addressing global sustainable energy challenges. Aerial imagery from drones and satellites plays an important role in monitoring and assessing these infrastructures. This study presents a comparative evaluation of three You Only Look Once (YOLO) model architectures, specifically YOLOv8-Large, YOLOv10-Large and YOLOv11-Large for the detection of solar panels and boilers. Through Exploratory Data Analysis (EDA), the patterns and characteristics of the dataset were identified, providing a strong basis for developing a robust object detection model. YOLOv10-Large demonstrated the best performance, achieving a precision of 89.2%, a recall of 81.3%, mAP (50) of 83.1%, and an F1 score of 85.0%. This advancement prioritizes efficiency and reliability as the backbone, contributing to more sustainable and resource-efficient practices in industries that rely on aerial imagery analysis.
About this Paper
Cite this paper as:
Koomson D., Abugre J., Moore S. (2025). YOLO algorithms for Solar Panel and Boiler Detection from Aerial Imagery. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIS25#06. https://doi.org/10.55432/978-1-6692-0011-6_11
Presented at:
The 2025 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025
Contact:
Douglas Koomson
douglaskojokoomson@gmail.com
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