| Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01069 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/202339101069 | |
| Published online | 05 June 2023 | |
Anomaly Detection in Solar Modules with Infrared Imagery
1 Professor, Department of Information Technology, GRIET, India
2,3,4,5 Student, Department of Information Technology, GRIET, India
* Corresponding author: nvgraju@griet.ac.in
Image classification is a machine learning task that involves assigning a label or class to an input image. In the context of the Infrared Solar Modules dataset, image classification can be used to identify anomalies in solar panel imagery. To achieve this goal, A convolutional neural network (CNN) model trained from scratch and fine-tuned on the Infrared Solar Modules dataset from ai4earthscience. Model includes techniques such as dropout and image data generation to enhance its accuracy on this specific dataset. With these methods, Model can achieve high accuracy in identifying solar panel anomalies even with low-size images.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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