TY  - JOUR
AU  - Saad Minhas
AU  - Zeba Khanam
AU  - Shoaib Ehsan
AU  - Klaus McDonald Maier
AU  - Aura Hernandez-Sabate
PY  - 2022//
TI  - Weather Classification by Utilizing Synthetic Data
T2  - SENS
JO  - Sensors
SP  - 3193
VL  - 22
IS  - 9
PB  - MDPI
KW  - Weather classification
KW  - synthetic data
KW  - dataset
KW  - autonomous car
KW  - computer vision
KW  - advanced driver assistance systems
KW  - deep learning
KW  - intelligent transportation systems
N2  - 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.
UR  - http://dx.doi.org/10.3390/s22093193
N1  - IAM; 600.139; 600.159; 600.166; 600.145;
ID  - Saad Minhas2022
ER  -