Weather Image Recognition Using Vision Transformer

Jun Zhi Tan, Jit Yan Lim, Kian Ming Lim, Chin Poo Lee

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

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.

Original languageEnglish
Title of host publication2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages50-55
Number of pages6
ISBN (Electronic)9798350340860
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th IEEE Conference on Systems, Process and Control, ICSPC 2023 - Malacca, Malaysia
Duration: 16 Dec 2023 → …

Publication series

Name2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings

Conference

Conference11th IEEE Conference on Systems, Process and Control, ICSPC 2023
Country/TerritoryMalaysia
CityMalacca
Period16/12/23 → …

Keywords

  • Deep Learning
  • Vision Transformer
  • ViT
  • Weather
  • Weather Recognition

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