Multibranch semantic image segmentation model based on edge optimization and category perception.

ABSTRACT: In semantic image segmentation tasks, most methods fail to fully use the characteristics of different scales and levels but rather directly perform upsampling. This may cause some effective information to be mistaken for redundant information and discarded, which in turn causes object segmentation confusion. As a convolutional layer deepens, the loss of spatial detail information makes the segmentation effect achieved at the object boundary insufficiently accurate. To address the above problems, we propose an edge optimization and category-aware multibranch semantic segmentation network (ECMNet). First, an attention-guided multibranch fusion backbone network is used to connect features with different resolutions in parallel and perform multiscale information interaction to reduce the loss

SUBMITTER: Yang Z 

PROVIDER: S-EPMC11658475 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature