Image-based Air Pollution Estimation Using Hybrid Convolutional Neural Network

摘要

Air pollution has a serious impact on our daily life, and how to quickly and easily measure the air pollution level without any expensive equipment is a quite challenging task. This paper proposes an air pollution estimation method using deep hybrid convolutional neural network from a single image, e.g., captured by a smartphone. The captured image is input to the main network, a very deep network, which solves the side effects of increased depth (degradation issues) by skip connection. This can improve network performance by simply increasing the depth of the network. Dark channel map is computed and fed into a secondary network to enrich the features with implicit representation. We have collected 1575 images of different scenes with different values of PM₂.₅ to train the network in the end-to-end fusion mode. Experimental results on synthetic dataset and real captured dataset demonstrate that our method achieves excellent performance on classification of air pollution levels from a single captured image.