Minhas, Saad and Khanam, Zeba and Ehsan, Shoaib and McDonald-Maier, Klaus and Hernández-Sabaté, Aura (2022) Weather Classification by Utilizing Synthetic Data. Sensors, 22 (9). p. 3193. DOI https://doi.org/10.3390/s22093193
Minhas, Saad and Khanam, Zeba and Ehsan, Shoaib and McDonald-Maier, Klaus and Hernández-Sabaté, Aura (2022) Weather Classification by Utilizing Synthetic Data. Sensors, 22 (9). p. 3193. DOI https://doi.org/10.3390/s22093193
Minhas, Saad and Khanam, Zeba and Ehsan, Shoaib and McDonald-Maier, Klaus and Hernández-Sabaté, Aura (2022) Weather Classification by Utilizing Synthetic Data. Sensors, 22 (9). p. 3193. DOI https://doi.org/10.3390/s22093193
Abstract
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.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Data Collection; Weather; Vision, Ocular; Neural Networks, Computer |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 27 Jun 2022 16:23 |
Last Modified: | 30 Oct 2024 16:30 |
URI: | http://repository.essex.ac.uk/id/eprint/33072 |
Available files
Filename: sensors-22-03193-v3.pdf
Licence: Creative Commons: Attribution 3.0