A Novel Method for the Recognition of Air Visibility Level Based on the Optimal Binary Tree Support Vector Machine

Naishan Zheng,Heyang Yao,Shikai Zhang,Manman Luo,Siyu Wang,Xinfa Qiu,Xiuguo Zou,Jiaqi Han,Jingxia Lu,Yuning Wei

doi:10.3390/atmos9120481

Abstract

As the traditional methods for the recognition of air visibility level have the disadvantages of high cost, complicated operation, and the need to set markers, this paper proposes a novel method for the recognition of air visibility level based on an optimal binary tree support vector machine (SVM) using image processing techniques. Firstly, morphological processing is performed on the image. Then, whether the region of interest (ROI) is extracted is determined by the extracted feature values, that is, the contrast features and edge features are extracted in the ROI. After that, the transmittance features of red, green and blue channels (RGB) are extracted throughout the whole image. These feature values are used to construct the visibility level recognition model based on optimal binary tree SVM. The experiments are carried out to verify the proposed method. The experimental results show that the recognition accuracies of the proposed method for four levels of visibility, i.e., good air quality, mild pollution, moderate pollution, and heavy pollution, are 92.00%, 92%, 88.00%, and 100.00%, respectively, with an average recognition accuracy of 93.00%. The proposed method is compared with one-to-one SVM and one-to-many SVM in terms of training time and recognition accuracy. The experimental results show that the proposed method can distinguish four levels of visibility at a relatively satisfactory level, and it performs better than the other two methods in terms of training time and recognition accuracy. This proposed method provides an effective solution for the recognition of air visibility level.

Highlights

Air visibility has a great impact on traffic, and it affects the safety of people’s travels
In order to overcome the shortcomings in the above methods, this paper proposes a novel method based on the optimal binary tree support vector machine (SVM) to recognize the air visibility level
Using the saliency map acquired in the frequency domain of the image, the region of interest (ROI) extracted by the saliency region is salient in the image, which can fully reflect the features of the image, so that the feature values extracted in the ROI can be distinguished

Summary

Introduction

Air visibility has a great impact on traffic, and it affects the safety of people’s travels. Timely detection of road visibility levels is of great significance for traffic safety, and relevant research has been extensively conducted both at home and abroad. The visibility level is usually detected using the transmission method [3] or the scattering method [4] in the optical principle. Gultepe et al [5] used optical sensors to estimate the air visibility from camera images. These optical monitoring instruments have the disadvantages of complicated installation, expensive cost, high requirements for surrounding environment, and complicated operation. The visual measurement method has the disadvantages of strong subjectivity and poor standardization, which severely limits the development of meteorological observation into an automatic way

Methods

Results

Conclusion