Anomaly Detection in Solar Modules with Infrared Imagery
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)...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01069.pdf |
| _version_ | 1797807817150693376 |
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| author | V Ganapathi Raju N. G Sai Narayana A Raja Sai G Vishnu Vardhan Rao Ch Yashwanth Reddy |
| author_facet | V Ganapathi Raju N. G Sai Narayana A Raja Sai G Vishnu Vardhan Rao Ch Yashwanth Reddy |
| author_sort | V Ganapathi Raju N. |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T06:28:19Z |
| format | Article |
| id | doaj.art-dbe1de3f72554735a24252cc9c4ff42b |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:28:19Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-dbe1de3f72554735a24252cc9c4ff42b2023-06-09T09:12:17ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013910106910.1051/e3sconf/202339101069e3sconf_icmed-icmpc2023_01069Anomaly Detection in Solar Modules with Infrared ImageryV Ganapathi Raju N.0G Sai Narayana1A Raja Sai2G Vishnu Vardhan Rao3Ch Yashwanth Reddy4Professor, Department of Information Technology, GRIETStudent, Department of Information Technology, GRIETStudent, Department of Information Technology, GRIETStudent, Department of Information Technology, GRIETStudent, Department of Information Technology, GRIETImage 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01069.pdf |
| spellingShingle | V Ganapathi Raju N. G Sai Narayana A Raja Sai G Vishnu Vardhan Rao Ch Yashwanth Reddy Anomaly Detection in Solar Modules with Infrared Imagery E3S Web of Conferences |
| title | Anomaly Detection in Solar Modules with Infrared Imagery |
| title_full | Anomaly Detection in Solar Modules with Infrared Imagery |
| title_fullStr | Anomaly Detection in Solar Modules with Infrared Imagery |
| title_full_unstemmed | Anomaly Detection in Solar Modules with Infrared Imagery |
| title_short | Anomaly Detection in Solar Modules with Infrared Imagery |
| title_sort | anomaly detection in solar modules with infrared imagery |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01069.pdf |
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