We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Enhancing Weather Scene Identification Using Vision Transformer.
- Authors
Dewi, Christine; Arshed, Muhammad Asad; Christanto, Henoch Juli; Rehman, Hafiz Abdul; Muneer, Amgad; Mumtaz, Shahzad
- 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 The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate.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.
- Subjects
TRANSFORMER models; COMPUTER vision; WEATHER forecasting; FEATURE extraction; INTELLIGENT networks
- Publication
World Electric Vehicle Journal, 2024, Vol 15, Issue 8, p373
- ISSN
2032-6653
- Publication type
Article
- DOI
10.3390/wevj15080373