SPHERE: Benchmarking YOLO vs. CNN on a Novel Dataset for High-Accuracy Solar Panel Defect Detection in Renewable Energy Systems

SPHERE: Benchmarking YOLO vs. CNN on a Novel Dataset for High-Accuracy Solar Panel Defect Detection in Renewable Energy Systems

Solar panels are critical for renewable electricity generation, yet defects significantly reduce power output and risk grid instability, necessitating reliable AI-driven defect detection. We propose the SPHERE (Solar Panel Hidden-Defect Evaluation for Renewable Energy) method for such cases. This st...

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Bibliographic Details
Main Authors: Kubilay Ayturan, Berat Sarıkamış, Mehmet Feyzi Akşahin, Uğurhan Kutbay
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Applied Sciences
Subjects:
solar panel classification
deep learning
YOLOv8
YOLOv9
convolutional neural networks
k-fold cross-validation
Online Access:https://www.mdpi.com/2076-3417/15/9/4880
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