Air Quality Classification and Measurement Based on Double Output Vision Transformer

Abstract

The atmospheric quality monitor is an essential component of atmospheric protection. However, using complicated large-scale chemical equipment required by traditional methods is difficult for ordinary people. We are committed to allowing ordinary people to obtain a local air quality index (AQI) and then participate in the air quality monitoring of the entire district quickly and easily. So, we propose a method improved from Transformer in this article. This method processes pictures taken by mobile devices and predicts a more accurate local AQI level. The proposed method performs better than traditional methods in frequency and flexibility. Tokens in the Double Output Vision Transformer (DOViT) proposed in this article will be processed with the multihead self-attention (MSA) mechanism of the model to automatically extract the features in pictures for achieving higher classification accuracy. The application of Transformer makes our model more concise and efficient than previous CNN-based methods.