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UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience, low cost and convenient maintenance. In marine environmental monitoring, the similarity between objects such as oil spill and sea surface, Spartina alterniflora and algae is high, and the effect of the general segmentation algorithm is poor, which brings new challenges to the segmentation of UAV marine images. Panoramic segmentation can do object detection and semantic segmentation at the same time, which can well solve the polymorphism problem of objects in UAV ocean images. Currently, there are few studies on UAV marine image recognition with panoptic segmentation. In addition, there are no publicly available panoptic segmentation datasets for UAV images. In this work, we collect and annotate UAV images to form a panoptic segmentation UAV dataset named UAV-OUC-SEG and propose a panoptic segmentation method named PanopticUAV. First, to deal with the large intraclass variability in scale, deformable convolution and CBAM attention mechanism are employed in the backbone to obtain more accurate features. Second, due to the complexity and diversity of marine images, boundary masks by the Laplacian operator equation from the ground truth are merged into feature maps to improve boundary segmentation precision. Experiments demonstrate the advantages of PanopticUAV beyond the most other advanced approaches on the UAV-OUC-SEG dataset. [ABSTRACT FROM AUTHOR]