Weather Classification by Utilizing Synthetic Data
Minhas, Saad (University of Essex. School of Computer Science and Electrical Engineering)
Khanam, Zeba (University of Essex. School of Computer Science and Electrical Engineering)
Ehsan, Shoaib (University of Essex. School of Computer Science and Electrical Engineering)
McDonald-Maier, Klaus (University of Essex. School of Computer Science and Electrical Engineering)
Hernandez-Sabaté, Aura (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)

Date:	2022
Description:	12 pàg.
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.
Grants:	Agencia Estatal de Investigación RTI2018-095209-B-C21 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1597
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Advanced driver assistance systems ; Autonomous car ; Computer vision ; Dataset ; Deep learning ; Intelligent transportation systems ; Synthetic data ; Weather classification
Published in:	Sensors (Basel, Switzerland), Vol. 22, Issue 9 (April 2022) , art. 3193, ISSN 1424-8220

DOI: 10.3390/s22093193
PMID: 35590881

12 p, 3.9 MB

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