Abstract
Semantic segmentation of low-altitude UAV imagery presents unique challenges due to extreme scale variations, complex object boundaries, and limited computational resources on edge devices. Existing transformer-based segmentation methods achieve remarkable performance but incur high computational overhead, while lightweight approaches struggle to capture fine-grained details in high-resolution aerial scenes. To address these limitations, we propose PBSeg, an efficient prototype-based segmentation framework tailored for UAV applications. PBSeg introduces a novel prototype-based cross-attention (PBCA) that exploits feature redundancy to reduce computational complexity while maintaining segmentation quality. The framework incorporates an efficient multi-scale feature extraction module that combines deformable convolutions (DConv) with context-aware modulation (CAM) to capture both local details and global semantics. Experiments on two challenging UAV datasets demonstrate the effectiveness of the proposed approach. PBSeg achieves 71.86\% mIoU on UAVid and 80.92\% mIoU on UDD6, establishing competitive performance while maintaining computational efficiency. Code is available at this https URL.
Abstract (translated)
低空无人机图像的语义分割面临独特挑战，包括极端的尺度变化、复杂的物体边界以及边缘设备上有限的计算资源。现有基于Transformer的分割方法虽性能卓越，但计算开销较高；而轻量级方法则难以捕捉高分辨率航拍场景中的细粒度细节。为应对这些限制，我们提出了PBSeg——一个专为无人机应用设计的高效原型分割框架。PBSeg引入了新颖的基于原型的交叉注意力机制（PBCA），通过利用特征冗余来降低计算复杂度，同时保持分割质量。该框架集成了高效的多尺度特征提取模块，结合可变形卷积（DConv）与上下文感知调制（CAM），以同时捕获局部细节与全局语义。在两个具有挑战性的无人机数据集上的实验验证了该方法的有效性：PBSeg在UAVid上达到71.86%的平均交并比（mIoU），在UDD6上达到80.92% mIoU，在保持计算效率的同时实现了具有竞争力的性能。代码已公开于此链接。
URL
https://arxiv.org/abs/2604.01550
