Image-Based PM_2.5 Estimation And Its Application On Depth Estimation

With the development of industry,people's quality of life is greatly improved,but the problem is that a large amount of aerosols,such as dust,sulfuric acid,nitric acid and other aerosols-haze,have a certain negative impact on people's lives.At present,all methods for classifying air pollution levels in recent years are based on certain pollution parameters,such as PM_2.5,PM₁₀,etc.PM_2.5refers to particulate matter with aerodynamic particle size of less than or equal to 2.5 microns in the atmosphere.It also becomes a particulate matter that can enter the lungs.It has a small particle size and is rich in a large amount of toxic and harmful substances.Real-time,efficient,low-cost estimation of atmospheric pollution is very necessary for people's daily lives.This paper focuses on image-based pollution level prediction,through deep convolutional neural networks and supervised learning methods.The main innovations are as follows.?1?This paper proposes a method for estimating air pollution from a single image using a deep hybrid convolutional neural network,for example,captured by a smart-phone.The captured image is input to the main network,which is a very deep network that resolves the side effects of increasing depth?gradient disappearance or explosion?by skipping connection.This can improve network performance by simply increasing the depth of the network.Dark channel maps are computed and fed into the secondary network to enrich the features using implicit representations.We use different PM_2.5values??to train the end-to-end network.Experimental results of synthetic data sets and actual captured data sets show that our method achieves excellent performance in the classification of air pollution levels for a single captured image.In addition,the high-level features are extracted based on the deep mixed convolutional neural network,and the mapping relationship between the features and PM_2.5is learned by using sup-port vector regression.Real-time estimation of PM_2.5can be achieved given a captured image.?2?This paper proposes a method for estimating the monocular depth in foul weather.Almost all existing monocular depth estimation algorithms focus on clean,pollution-free images,but not for data images in harsh weather such as fog,haze,etc.To estimate the depth of the captured scene,we first compute the transmission map us-ing sparse prior and non-local bilateral kernel,and then estimate the depth through the atmospheric scattering model with the estimated PM_2.5.Experimental results demon-strate that the proposed method achieves the same level of PM_2.5-estimation accuracy as commodity measuring device,and can estimate plausible depth information that is even better than the“ground-truth”captured by a laser in the no-haze condition.This could be very useful in many applications under polluted air conditions.