Panoptic segmentation network based on fusion coding and attention mechanism

Abstract

Aiming at the problem that the panoptic segmentation network based on coding structure can't accurately extract the detailed information of panoptic images, considering that there are some commonalities between semantic segmentation and instance segmentation tasks, this paper proposes a panoptic segmentation model with multi-feature fusion structure, which generates multi-scale fused feature maps for the panoptic segmentation network, uses the Atrous Spatial Pyramid Pooling to preferentially process the high-level features with rich context information, and then uses the cascade method to splice the low-level features to improve the panoptic segmentation performance of the model. By adding coordinate attention to the ASPP module of the corresponding branch, the perception ability of the model to the contour and instance center is enhanced.