A deep learning model to detect foggy images for vision enhancement

Abstract

ABSTRACT Fog limits meteorological visibility, posing a significant danger to road safety. Poor visibility is considered as a significant contributor to road accidents in foggy weather conditions. Regardless, image defogging techniques can only work with foggy images. In a real-time system, however, defogging foggy images becomes difficult if they are not identified as foggy or clear. Because we cannot rely on human vision to distinguish between foggy and clear pictures, we need a robust model that classifies the input image as foggy or clear based on some features. This paper proposes a robust Deep Learning (DL) model based on Convolutional Neural Network (CNN) for classifying the input as foggy and clear. The proposed Deep Neural Network (DNN) architecture is efficient and precise enough to classify images as foggy or clear, with a training time complexity of and a prediction time complexity of . The experimental results reveal promising results in both qualitative and quantitative assessments. The model has an accuracy of 94.8%, precision of 91.8%, recall of 75.8% and F1 score of 80.3% when evaluated on the SOTS dataset, indicating that it might be utilized to mitigate the safety risk in vision enhancement systems.