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Author
Saad Minhas; Zeba Khanam; Shoaib Ehsan; Klaus McDonald Maier; Aura Hernandez-Sabate
Title
Weather Classification by Utilizing Synthetic Data
Type
Journal Article
Year
2022
Publication
Sensors
Abbreviated Journal
SENS
Volume
22
Issue
9
Pages
3193
Keywords
Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; 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.
Address
21 April 2022
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MDPI
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Notes
IAM; 600.139; 600.159; 600.166; 600.145;
Approved
no
Call Number
Admin @ si @ MKE2022
Serial
3761
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