Panoramic segmentation method, system and device based on graph neural network and storage medium

Abstract

The invention discloses a panoramic segmentation method based on a graph neural network. The panoramic segmentation method comprises the following steps: extracting a plurality of target features froma picture; segmenting the head network through an example to obtain a foreground category probability, a background category probability and a mask result of the picture, and semantically segmentingthe head network to obtain a preliminary semantic segmentation result of the picture; processing the new foreground image through the foreground category probability to generate an instance classification result, and extracting a target instance segmentation mask from the instance classification result according to a mask result; processing the new background image through the background categoryprobability and the preliminary semantic segmentation result to generate a target semantic segmentation result; and fusing the target instance segmentation mask and the target semantic segmentation result by adopting a heuristic algorithm to generate a panoramic segmentation result. The invention further discloses a panoramic segmentation system based on the graph neural network, computer equipment and a computer readable storage medium. By adopting the method and the device, the panoramic segmentation effect of the picture can be optimized by utilizing the mutual relation between the objects.

Description

technical field [0001] The present invention relates to the technical field of image data processing, in particular to a panorama segmentation method based on a graph neural network, a panorama segmentation system based on a graph neural network, computer equipment, and a computer-readable storage medium. Background technique [0002] Image segmentation technology is a research hotspot in the field of computer vision. It has a very wide range of applications in all aspects of people's lives, such as map construction in the field of autonomous driving, automatic diagnosis in the field of medical imaging, and virtual testing in daily life. wear etc. [0003] Image segmentation technology is divided into semantic segmentation (Semantic Segmentation), instance segmentation (InstanceSegmentation) and panoramic segmentation (Panoptic Segmentation). in: [0004] Semantic segmentation requires assigning a class label to each pixel in the image, but does not distinguish between dif...

AI Technical Summary

Problems solved by technology

Therefore, the prediction effect of the existing panoramic segmentation technology is not good, and misjudgment often occurs.

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