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

PLoS One. 2024 Dec 19;19(12):e0315621. doi: 10.1371/journal.pone.0315621. eCollection 2024.

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 of spatial detail information. Second, a category perception module is used to learn category feature representations and guide the pixel classification process through an attention mechanism to optimize the resulting segmentation accuracy. Finally, an edge optimization module is used to integrate the edge features into the middle and the deep supervision layers of the network through an adaptive algorithm to enhance its ability to express edge features and optimize the edge segmentation effect. The experimental results show that the MIoU value reaches 79.2% on the Cityspaces dataset and 79.6% on the CamVid dataset, that the number of parameters is significantly lower than those of other models, and that the proposed method can effectively achieve improved semantic image segmentation performance and solve the partial category segmentation confusion problem, giving it certain application prospects.

MeSH terms

  • Algorithms*
  • Humans
  • Image Processing, Computer-Assisted* / methods
  • Neural Networks, Computer
  • Semantics*

Grants and funding

This study was funded by the National Natural Science Foundation of China (Grant No. 62372397) and the Shanxi Province Natural Fund Project (Grant Nos. 202203021221222, 202203021221229). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.