轻量级无人机图像语义分割新框架，兼顾精度与效率
Prototype-Based Low Altitude UAV Semantic Segmentation
- 用原型交叉注意力减少计算开销，保留分割质量
- 在UAVid和UDD6上分别达71.86%和80.92% mIoU
- 适合边缘设备部署，适合高分辨率航拍场景
低空无人机影像的语义分割面临尺度变化剧烈、物体边界复杂及边缘设备算力有限等挑战。现有基于Transformer的方法虽性能优异但计算开销大，轻量化方法又难以捕捉高分辨率航拍图像中的细粒度细节。为此，我们提出PBSeg，一种专为无人机应用设计的高效原型基分割框架。该框架引入新型原型交叉注意力（PBCA），利用特征冗余降低计算复杂度，同时保持分割精度。结合可变形卷积（DConv）与上下文感知调制（CAM）的多尺度特征提取模块，有效捕获局部细节与全局语义。在两个挑战性无人机数据集上的实验表明，PBSeg在UAVid上达到71.86% mIoU，UDD6上达80.92% mIoU，性能领先且计算高效。代码已开源。
原文摘要 · Abstract (English)
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 https://github.com/zhangda1018/PBSeg.
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