@Article{SaadMinhas2022,
author="Saad Minhas
and Zeba Khanam
and Shoaib Ehsan
and Klaus McDonald Maier
and Aura Hernandez-Sabate",
title="Weather Classification by Utilizing Synthetic Data",
journal="Sensors",
year="2022",
publisher="MDPI",
volume="22",
number="9",
pages="3193",
optkeywords="Weather classification",
optkeywords="synthetic data",
optkeywords="dataset",
optkeywords="autonomous car",
optkeywords="computer vision",
optkeywords="advanced driver assistance systems",
optkeywords="deep learning",
optkeywords="intelligent transportation systems",
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.",
optnote="IAM; 600.139; 600.159; 600.166; 600.145;",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3761), last updated on Tue, 25 Apr 2023 15:35:21 +0200",
doi="10.3390/s22093193"
}