A Road Segmentation Method Based on Dynamic Attention and Cross-Scale Semantic Fusion

Abstract

To address the poor adaptability to complex scenes, blurred boundary details, and the difficult trade-off between accuracy and computational cost in road segmentation from unmanned aerial vehicle (UAV) imagery, this paper proposes DACS-Net, a lightweight road segmentation model based on U-Net and enhanced by dynamic attention and cross-scale semantic information. The model adopts EfficientNetV2-S as a lightweight and efficient encoder, using compound scaling and MBConv bottleneck structures to capture multi-scale features accurately. A dynamic global-local attention module (GLA) is designed to jointly model global context and local neighborhood details through a dynamic global attention branch and an adaptive local branch. Furthermore, a cross-scale semantic feature fusion module (CSF) is constructed to perform intelligent selection and efficient fusion of multi-stage encoder features through semantic-guided weight allocation, edge-enhanced feature alignment, and lightweight channel compression. Experiments on the public AeroScapes dataset show that the proposed method outperforms mainstream models in Dice, Kappa, and other metrics while maintaining relatively low computational complexity, demonstrating its effectiveness and practical value.