An object detection method for heavy fog scenes based on image defogging and sample enhancement
Shufang Xu, Yaowen Fu, Wenpeng Zhang, Xiaoyi Sun · 2022
Deep convolutional neural network has achieved superior recognition performance on many public object detection datasets. However, under the weather conditions of rain or fog, the scarcity of samples has always been the problems restricting the accuracy of detection and identification. To solve this problem, this paper proposed an object detection method for heavy fog scenes based on image defogging and sample enhancement. Firstly, generative adversarial network (GAN) is adopted to remove the fog from images, and then achieve sample enhancement by a style transfer network, which keeps the image content basically unchanged and transform the style of image texture. Fog-free dataset after sample enhancement can reduce the influence of the texture information on the network model and make it pay more attention to the contour information of the object shape. The experimental results on I-HAZE and REISDE dataset show that our proposed method can effectively improve the object detection precision and the mAP (mean average precision) can be improved by up to 15%.