@inproceedings{bfb0ccf1ddf74b83a180b22f8d99a90f,
title = "Weather Image Recognition Using Vision Transformer",
abstract = "Weather significantly impacts human activities, and accurate weather recognition is crucial to mitigate the risks associated with severe weather conditions. In this research project, we propose Vision Transformer for weather image recognition. The goal is to identify weather patterns and conditions accurately to enhance safety in activities that are affected by the weather. To demonstrate the performance, additional five methods have been adopted to carry out the comparison, including K-Nearest Neighbors, Random Forest, Convolutional Neural Networks, Residual Network, and Compact Convolutional Transformer. Our experimental results show that the proposed Vision Transformer model achieved the highest accuracy of 99.58%, which outperformed the other models. This finding highlights the potential of deep learning techniques for accurate weather recognition.",
keywords = "Deep Learning, Vision Transformer, ViT, Weather, Weather Recognition",
author = "Tan, {Jun Zhi} and Lim, {Jit Yan} and Lim, {Kian Ming} and Lee, {Chin Poo}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th IEEE Conference on Systems, Process and Control, ICSPC 2023 ; Conference date: 16-12-2023",
year = "2023",
doi = "10.1109/ICSPC59664.2023.10420033",
language = "English",
series = "2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "50--55",
booktitle = "2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings",
address = "United States",
}