PT Journal
AU Saad Minhas
   Zeba Khanam
   Shoaib Ehsan
   Klaus McDonald Maier
   Aura Hernandez-Sabate
TI Weather Classification by Utilizing Synthetic Data
SO Sensors
JI SENS
PY 2022
BP 3193
VL 22
IS 9
DI 10.3390/s22093193
DE Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems
AB 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.
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