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Prasit Nangthin. Deep convolutional neural networks for accurate solar module classification in thermal images. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2024.
Deep convolutional neural networks for accurate solar module classification in thermal images
Name: Chutinun Phungket
Organization :
Pathumwan Institute of Technology. Faculty of Engineering
Email :
chutinun@pit.ac.th
Name: Prasit Nangthin
Organization :
Pathumwan Institute of Technology. Faculty of Engineering
Email :
prasit@pit.ac.th
keyword:
Convolutional neural networks
LCSH:
Solar cells
LCSH:
Infrared imaging
Abstract:
The rapid and continuous technological
advancements in computer and internet technologies, combined
with data management techniques, find applications in various
domains. For example, they aid in predictive maintenance
systems, evaluate the performance of solar modules, and
differentiate efficiently performing solar modules from less
efficient ones using thermal imagery. The accuracy of solar
module classification significantly impacts the subsequent
assessment. In this study, we employ advanced techniques,
particularly deep learning through Convolutional Neural
Networks (CNN), to classify thermal images of solar modules.
The practical aspects of applying CNN to the dataset of thermal
images are explored, including transfer learning from pretrained
CNN architectures and custom CNN architecture
training. Experiments are conducted with three prominent
types of solar module conditions: normal, cracked, and dusty.
The results reveal that the most effective approach involves
learning and fine-tuning pre-trained architectures on detailed
studies, achieving a classification accuracy of up to 94.5%.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2025-01-23
Issued:
2025-01-23
บทความ/Article
application/pdf
BibliograpyCitation :
In Rajamangala University of Technology Krungthep. 12th International Electrical Engineering Congress (iEECON 2024) (pp.302-306). Bangkok : Rajamangala University of Technology Krungthep
eng
©copyrights King Mongkut's University of Technology North Bangkok
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ใช้เวลา
0.041159 วินาที
Chutinun Phungket
| Title | Contributor | Type |
|---|---|---|
| Deep convolutional neural networks for accurate solar module classification in thermal images
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chutinun Phungket;Prasit Nangthin | บทความ/Article | |
| Transfer learning evaluation of convolutional neural network architectures for thermal image classification of solar modules
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chutinun Phungket;Prasit Nangtin | บทความ/Article |
Prasit Nangthin
| Title | Contributor | Type |
|---|---|---|
| Deep convolutional neural networks for accurate solar module classification in thermal images
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chutinun Phungket;Prasit Nangthin | บทความ/Article |