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Weather Classification by Utilizing Synthetic Data
Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105758/ https://www.ncbi.nlm.nih.gov/pubmed/35590881 http://dx.doi.org/10.3390/s22093193 |
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author | Minhas, Saad Khanam, Zeba Ehsan, Shoaib McDonald-Maier, Klaus Hernández-Sabaté, Aura |
author_facet | Minhas, Saad Khanam, Zeba Ehsan, Shoaib McDonald-Maier, Klaus Hernández-Sabaté, Aura |
author_sort | Minhas, Saad |
collection | PubMed |
description | Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets. |
format | Online Article Text |
id | pubmed-9105758 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91057582022-05-14 Weather Classification by Utilizing Synthetic Data Minhas, Saad Khanam, Zeba Ehsan, Shoaib McDonald-Maier, Klaus Hernández-Sabaté, Aura Sensors (Basel) Article Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets. MDPI 2022-04-21 /pmc/articles/PMC9105758/ /pubmed/35590881 http://dx.doi.org/10.3390/s22093193 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Minhas, Saad Khanam, Zeba Ehsan, Shoaib McDonald-Maier, Klaus Hernández-Sabaté, Aura Weather Classification by Utilizing Synthetic Data |
title | Weather Classification by Utilizing Synthetic Data |
title_full | Weather Classification by Utilizing Synthetic Data |
title_fullStr | Weather Classification by Utilizing Synthetic Data |
title_full_unstemmed | Weather Classification by Utilizing Synthetic Data |
title_short | Weather Classification by Utilizing Synthetic Data |
title_sort | weather classification by utilizing synthetic data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105758/ https://www.ncbi.nlm.nih.gov/pubmed/35590881 http://dx.doi.org/10.3390/s22093193 |
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