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Weather Classification by Utilizing Synthetic Data.

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. ISSN 1424-8220

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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: Elements
Depositing User: Elements
Date Deposited: 27 Jun 2022 16:23
Last Modified: 27 Jun 2022 16:24
URI: http://repository.essex.ac.uk/id/eprint/33072

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