A Deep Learning Framework for Boundary-Aware Semantic Segmentation
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
As a fundamental task in computer vision, semantic segmentation is widely
applied in fields such as autonomous driving, remote sensing image analysis,
and medical image processing. In recent years, Transformer-based segmentation
methods have demonstrated strong performance in global feature modeling.
However, they still struggle with blurred target boundaries and insufficient
recognition of small targets. To address these issues, this study proposes a
Mask2Former-based semantic segmentation algorithm incorporating a boundary
enhancement feature bridging module (BEFBM). The goal is to improve target
boundary accuracy and segmentation consistency. Built upon the Mask2Former
framework, this method constructs a boundary-aware feature map and introduces a
feature bridging mechanism. This enables effective cross-scale feature fusion,
enhancing the model's ability to focus on target boundaries. Experiments on the
Cityscapes dataset demonstrate that, compared to mainstream segmentation
methods, the proposed approach achieves significant improvements in metrics
such as mIOU, mDICE, and mRecall. It also exhibits superior boundary retention
in complex scenes. Visual analysis further confirms the model's advantages in
fine-grained regions. Future research will focus on optimizing computational
efficiency and exploring its potential in other high-precision segmentation
tasks.
