Dehaze-Seg: A Multi-task Learning Framework for Collaborative Dehazing and Semantic Segmentation

Many Unmanned Aerial Vehicles (UAVs) utilize the visible-light images for environmental perception, making them particularly vulnerable to the degradation of image quality caused by haze or fog. This paper presents a comprehensive solution to enhance the UAV vision specifically in collaborative dehazing and semantic segmentation through three contributions: (1) We propose a multi-task learning framework named Dehaze-Seg integrating U-shaped restoration backbone with frequencyspatial feature fusion to jointly optimize dehazing and segmentation tasks,which results into synchronous output of dehazed images and semantic labels; (2) A multi-density foggy image synthesizing method is proposed by employing metric-scale depth estimation and truncated normal distributions for atmospheric parameter sampling. This results into two proposed multi-density benchmark datasets with synthetic fog, namely HazyUAVid and HazyUDD, respectively; (3) A cyclical network training strategy specifically for robust adaptation to varying fog distribution is proposed. Experimental results demonstrate significant improvements over state-of-the-art methods in both dehazing and semantic segmentation tasks, with powerful generalization among UAV images. We also find that our dehazing module still works well in the ground perspective such as road scene. Codes and datasets are available at https://github.com/aresdrw/dehaze-seg.