Detection of agglomerate fog based on a shallow convolutional neural network

Linlin Li, Bo Yang, Shaohui Chen

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.