Multi-Weather Classification using Evolutionary Algorithm on EfficientNet

Zhang, Yulong; Sun, Jianyong; Chen, Mingkang; Wang, Qiang; Yuan, Yuan; Ma, Rongzhe

doi:10.1109/percomworkshops51409.2021.9430939

Cited by 7 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming that the block image size is set to P × P, then N blocks are obtained after dividing the image, which is the length of the sequence input to the transformer encoder. The calculation process is shown in Equation (1).…”

Section: Vision Transformer Pre-trained Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Study of Weather-Image Classification Combining VIT and a Dual Enhanced-Attention Module

Luo

2023

Electronics

View full text Add to dashboard Cite

A weather-image-classification model combining a VIT (vision transformer) and dual augmented attention module is proposed to address the problems of the insufficient feature-extraction capability of traditional deep-learning methods with the recognition accuracy still to be improved and the limited types of weather phenomena existing in the dataset. A pre-trained model vision transformer is used to acquire the basic semantic feature representation of weather images. Dual augmented attention combined with convolutional self-attention and Atrous self-attention modules are used to acquire the low-level and high-level deep-image semantic representations, respectively, and the feature vectors are spliced and fed into the linear layer to obtain the weather types. Experimental validation is performed on the publicly available standard weather-image datasets MWD (Multi-class Weather Database) and WEAPD (Weather Phenomenon Database), and the two datasets are combined to enhance the comprehensiveness of the model for weather-phenomena recognition. The results show that the model achieves the highest F1 scores of 97.47%, 87.69% and 92.73% on the MWD, WEAPD and merged datasets, respectively. These scores are higher than the scores of recent deep-learning models with excellent performance in the experimental comparison, thereby, proving the effectiveness of the model.

show abstract

Section: Vision Transformer Pre-trained Modelmentioning

confidence: 99%

“…Weather-phenomenon detection plays a critical role in various applications, such as weather forecasting, road monitoring, transportation and agriculture [1][2][3]. Limited research has been performed on weather-image classification, which is different from ordinary image-classification tasks and more difficult.…”

Section: Introductionmentioning

confidence: 99%