%0 Journal Article
%T Weather Classification by Utilizing Synthetic Data
%A Saad Minhas
%A Zeba Khanam
%A Shoaib Ehsan
%A Klaus McDonald Maier
%A Aura Hernandez-Sabate
%J Sensors
%D 2022
%V 22
%N 9
%I MDPI
%F Saad Minhas2022
%O IAM; 600.139; 600.159; 600.166; 600.145;
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3761), last updated on Tue, 25 Apr 2023 15:35:21 +0200
%X 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.
%K Weather classification
%K synthetic data
%K dataset
%K autonomous car
%K computer vision
%K advanced driver assistance systems
%K deep learning
%K intelligent transportation systems
%U http://dx.doi.org/10.3390/s22093193
%P 3193