Saliency Detection Algorithm for Foggy Images Based on Deep Learning

Abstract

The detection of salient objects in foggy scenes is an important research component in many practical applications such as action recognition, target tracking and pedestrian re-identification. To facilitate saliency detection in foggy scenes, this paper explores two issues. The construction of dataset for foggy weather conditions and implementation scheme for foggy weather saliency detection. Firstly, a foggy sky image synthesis method is designed based on the atmospheric scattering model, and a saliency detection dataset applicable to foggy sky is constructed. Secondly, we compare the current classification networks and adopt resnet50, which has the highest classification accuracy, as the backbone network of the classification module, and classify the foggy sky images into three levels, namely fogless, light fog and dense fog, according to different concentrations. Then, Residual Refinement Network (R²Net) was selected to train and test the classified images. Horizontal and vertical flipping and image cropping were used to enhance the training set to relieve over-fitting. The accuracy of the network model was improved by using Adam as the optimizer. Experimental results show that for the detection of fogless images, our method is almost on par with state-of-the-art, and performs well for both light and dense fog images. Our method has good adaptability, accuracy and robustness.