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.

Air pollution detection and measurement is an important problem. Fast and effective explanation of the air quality is a necessary technique for monitoring the air pollution. However, the existing air quality measurement devices severely rely on many sensors, which are not only expensive but also inconvenient to carry. With the development of deep learning technology, computer vision-based task, such as the video processing and understanding have achieved great progress. Recently, image-based air quality measuring methods have been proposed and achieved satisfying accuracy in specific scenes. Whereas, the performance of those methods are not stable due to the noises in images and missed temporal relations between single images. With the rising of the short-video platform, the acquisition and dissemination of video data becomes more convenient. To address the problems of image-based air pollution measurement, we propose a video-based dual-channel 3-D convolution network for stable and accurate measuring. Besides the basic visual channel, we add a semantic channel to guide the network learn region-level features. The features from two channels are combined for stable prediction. Moreover, to evaluate the effectiveness of our method, we collect an outdoor video data set for air quality measurement through smart phones. Extensive experimental results show that our method achieves good results in the air quality measurement and outperforms the image-based methods.