Panoptic segmentation requires the prediction of a pixel-level mask with a category label in an image. And it is the fusion of semantic and instance segmentation, which focuses on segmenting both stuff and things simultaneously. In segmentation tasks, stuff refers to amorphous and uncountable regions such as grass, sky, and road. In addition, things include countable instances such as persons and cars. In recent years, panoptic segmentation has been gaining more attention since it can help us understand objects and the environment in many fields, such as medical images, remote sensing images, and autonomous driving.