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Photovoltaic defect classification through thermal infrared imaging using a machine learning approach
Author(s) -
Dunderdale Christopher,
Brettenny Warren,
Clohessy Chantelle,
Dyk E. Ernest
Publication year - 2020
Publication title -
progress in photovoltaics: research and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.286
H-Index - 131
eISSN - 1099-159X
pISSN - 1062-7995
DOI - 10.1002/pip.3191
Subject(s) - scale invariant feature transform , photovoltaic system , artificial intelligence , pattern recognition (psychology) , computer science , classifier (uml) , random forest , infrared , support vector machine , feature (linguistics) , thermal infrared , machine learning , feature extraction , computer vision , engineering , physics , optics , electrical engineering , linguistics , philosophy
This study examines a deep learning and feature‐based approach for the purpose of detecting and classifying defective photovoltaic modules using thermal infrared images in a South African setting. The VGG‐16 and MobileNet models are shown to provide good performance for the classification of defects. The scale invariant feature transform (SIFT) descriptor, combined with a random forest classifier, is used to identify defective photovoltaic modules. The implementation of this approach has potential for cost reduction in defect classification over current methods.

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