Enhancing Weather Scene Identification Using Vision Transformer
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Published:2024-08-16
Issue:8
Volume:15
Page:373
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ISSN:2032-6653
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Container-title:World Electric Vehicle Journal
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language:en
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Short-container-title:WEVJ
Author:
Dewi Christine12ORCID, Arshed Muhammad Asad3ORCID, Christanto Henoch Juli4ORCID, Rehman Hafiz Abdul3, Muneer Amgad5ORCID, Mumtaz Shahzad6ORCID
Affiliation:
1. Department of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia 2. School of Information Technology, Deakin University, Campus, 221 Burwood Hwy, Burwood, VIC 3125, Australia 3. School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan 4. Department of Information System, Atma Jaya Catholic University of Indonesia, Jakarta 12930, Indonesia 5. Department of Computer Science and Information Sciences, Universiti Teknologi PETRONS, Seri Iskandar 32160, Malaysia 6. School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK
Abstract
The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life highlights the vital necessity for accurate information. Precise weather detection is especially crucial for industries like intelligent transportation, outside vision systems, and driverless cars. The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate. Unmatched accuracy is required for local weather scene forecasting in real time. This work utilizes the capabilities of computer vision to address these important issues. Specifically, we employ the advanced Vision Transformer model to distinguish between 11 different weather scenarios. The development of this model results in a remarkable performance, achieving an accuracy rate of 93.54%, surpassing industry standards such as MobileNetV2 and VGG19. These findings advance computer vision techniques into new domains and pave the way for reliable weather scene recognition systems, promising extensive real-world applications across various industries.
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