Convolution Neural Network (CNN) Architectures Analysis for Photovoltaic (PV) Module Defect Images Classification
Author:
Affiliation:
1. School of Electrical Engineering, College of Engineering Universiti Teknologi MARA,Selangor,Malaysia,40450
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10037245/10037249/10037564.pdf?arnumber=10037564
Reference24 articles.
1. Deep Learning Based Detection of Cracks in Electroluminescence Images of Fielded PV modules
2. Automatic classification of defective photovoltaic module cells in electroluminescence images
3. ResNet (34, 50, 101): Residual CNNs for Image Classification Tasks;[Online] Available,0
4. CNN-based Deep Learning Approach for Micro-crack Detection of Solar Panels
5. A Transfer Learning Model Based on ResNet-50 for Flower Detection
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1. Convolutional Neural Network (CNN) Technology to Detect Welding Defects in Motorcycle Frames using Transfer Learning and Optimizer;2023 International Workshop on Artificial Intelligence and Image Processing (IWAIIP);2023-12-01
2. Photovoltaic Module Defects Classification Analysis Using ShuffleNet Architecture in Electroluminescence Images;2023 9th International Conference on Computer and Communication Engineering (ICCCE);2023-08-15
3. Photovoltaic (PV) Module Defect Image Classification Analysis Using EfficientNetV2 Architectures;2023 IEEE 14th Control and System Graduate Research Colloquium (ICSGRC);2023-08-05
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