Feature Enhancement And Fusion For Image-Based Particle Matter Estimation With F-Mse Loss

Air pollution is a major hazard to environment and human health. Particle matter with a diameter less than 2.5 micrometers (PM2.5) is a very harmful air pollutant that can penetrate deeply into lungs through human respiratory system. In this paper, we propose an efficient and reliable method to estimate PM2.5 concentration using outdoor images. Firstly, a prior attention block based on gradient features is used to enhance the boundary area between the sky region and the object in a feature map. After that, an embedding layer is applied to encode weather information and fuse it with image features. Finally, a deep neural network model with a novel loss function, F-MSE, is constructed to combine the prediction error of each model level during the training process and to further improve the effectiveness of the presented method. The proposed method was evaluated on a PM2.5 dataset with 1,514 images and the experimental results demonstrate that our method outperformed other existing methods.