Detection of agglomerate fog based on a shallow convolutional neural network

Linlin Li,Shaohui Chen,Bo Yang

doi:10.1007/s11042-021-11540-5

Abstract

As a kind of frequent bad weather, Agglomerate fog is a serious danger to people's safe driving, especially on the highway. Therefore, the research on the detection of fog is of great practical significance to ensure the safety of pedestrians. This paper proposes a shallow convolutional neural network for agglomerate fog detection in images, including the framework of the network and the detailed design of each component. Firstly, the image is divided into several sub-images; and then a shallow convolutional neural network is constructed and employed to identify the existence of fog for each of the sub-area images; lastly, the decision results of each sub-area images were integrated to determine whether the whole image contained agglomerate fog. A large quantity of simulation data and real data were used to test the performance of the proposed method, the experimental results show that the presented method can achieve more than 90% detection accuracy, which demonstrated that the advantage of the proposed method comparing with several existed methods.

Highlights

Agglomerate fog is generally found in low-lying areas with high air humidity, which is closely related to local microclimatic environment
This paper proposes a shallow convolutional neural network model for agglomerate fog detection based on image
With the characteristics of fast and accurate emergency response, this algorithm can play a role in disaster prevention and mitigation, and provide safety services for vehicle travel, which has important practical application value

Summary

Introduction

Agglomerate fog is generally found in low-lying areas with high air humidity, which is closely related to local microclimatic environment. There were many fog detection approaches were developed through machine learning [23] The former approaches designed the corresponding methods by analyzing the influence of agglomerate fog on objects in the scene and to detect the agglomerate fog. The latter approach trains classifiers through artificial features to identify agglomerate fog. The key issue of these two kinds of methods lies in the construction of features and the design of classifiers. Both two kinds of agglomerate fog detection methods have been greatly improved in terms of accuracy

Methods

Results

Conclusion