A domain‐adaptive method with cycle perceptual consistency adversarial networks for vehicle target detection in foggy weather

Abstract

Foggy weather can cause such problems as blurred image information and the loss of image details, which may pose great challenges to road traffic target detection based on images and videos. In this study, we propose a domain-adaptive road vehicle target detection method to implement domain adaptation for the real foggy scene. We firstly constructed a highway vehicle detection dataset with foggy images (HVFD), which contains normal weather images and foggy images and provides a complete data support for vehicle detection based on computer vision. Secondly, by improving CycleGAN we designed an improved generative confrontation network (CPGAN), which realised the style transfer between foggy images and normal weather images. Finally, we formulated a YOLOv4 target detection framework according to the domain adaptation based on the pre-trained YOLOv4 fog vehicle detection model. The experimental results show that the method we put forward can effectively improve vehicle detection performance and reduce the work of manually labelling a large number of foggy image tags, which has a strong generalisation ability for computer vision-based applications in low-visibility weather.