火力与指挥控制2026，Vol.51Issue(2)：54-60,69,8.DOI:10.3969/j.issn.1002-0640.2026.02.008
一种无人机航拍图像多尺度目标语义分割方法
A Multi-scale Target Semantic Segmentation Method for UAV Aerial Images
摘要
Abstract
To address the limitations of small target segmentation accuracy and cross-scale feature alignment in multi-scale target semantic segmentation tasks for UAV aerial images,this paper proposes the TransSeg model.The model integrates the global modeling capability of Swin Transformer with the symmetric encoder-decoder architecture of U-Net,and introduces D-ASPP and residual attention mechanisms.The loss function employs a collaborative optimization strategy combining Dice Loss and Focal Loss.Experiments were conducted on the UAVid dataset,with comparisons made against mainstream models including DANet,BiSeNet,and SegFormer,validating the effectiveness of TransSeg in segmenting 8 types of urban scene targets,and demonstrating strong performance on a custom dataset.The results show that the model achieves an average IoU of 66.9％,an improvement of 0.9％over SegFormer,with IoU increases of 4.1％and 5.6％for small targets such as moving vehicles and pedestrians,respectively.The research highlights the critical role of global context modeling and dynamic multi-scale fusion mechanisms in enhancing segmentation performance,providing a new solution for UAV remote sensing image processing.关键词
无人机航拍图像/语义分割/多尺度目标/Swin Transformer/D-ASPP/残差注意力机制Key words
UAV aerial images/semantic segmentation/multi-scale targets/Swin Transformer/D-ASPP/residual attention mechanism分类
航空航天引用本文复制引用
张蔚..一种无人机航拍图像多尺度目标语义分割方法[J].火力与指挥控制,2026,51(2):54-60,69,8.基金项目
河南省教育厅资助项目(ZJC18020,ZJC17098) （ZJC18020,ZJC17098）
