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....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/9/3193 |
| _version_ | 1797502910735581184 |
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| author | Saad Minhas Zeba Khanam Shoaib Ehsan Klaus McDonald-Maier Aura Hernández-Sabaté |
| author_facet | Saad Minhas Zeba Khanam Shoaib Ehsan Klaus McDonald-Maier Aura Hernández-Sabaté |
| author_sort | Saad Minhas |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T03:42:55Z |
| format | Article |
| id | doaj.art-5751f037df564c35bc5d0ddcc7b11525 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:42:55Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5751f037df564c35bc5d0ddcc7b115252023-11-23T09:14:45ZengMDPI AGSensors1424-82202022-04-01229319310.3390/s22093193Weather Classification by Utilizing Synthetic DataSaad Minhas0Zeba Khanam1Shoaib Ehsan2Klaus McDonald-Maier3Aura Hernández-Sabaté4School of Computer Science & Electrical Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UKSchool of Computer Science & Electrical Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UKSchool of Computer Science & Electrical Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UKSchool of Computer Science & Electrical Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UKComputer Vision Centre, Universitat Autònoma de Barcelona, Plaça Cívica, 08193 Bellaterra, SpainWeather 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.https://www.mdpi.com/1424-8220/22/9/3193weather classificationsynthetic datadatasetautonomous carcomputer visionadvanced driver assistance systems |
| spellingShingle | Saad Minhas Zeba Khanam Shoaib Ehsan Klaus McDonald-Maier Aura Hernández-Sabaté Weather Classification by Utilizing Synthetic Data Sensors weather classification synthetic data dataset autonomous car computer vision advanced driver assistance systems |
| 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 | weather classification synthetic data dataset autonomous car computer vision advanced driver assistance systems |
| url | https://www.mdpi.com/1424-8220/22/9/3193 |
| work_keys_str_mv | AT saadminhas weatherclassificationbyutilizingsyntheticdata AT zebakhanam weatherclassificationbyutilizingsyntheticdata AT shoaibehsan weatherclassificationbyutilizingsyntheticdata AT klausmcdonaldmaier weatherclassificationbyutilizingsyntheticdata AT aurahernandezsabate weatherclassificationbyutilizingsyntheticdata |