提升可行驶区域分割边界精度，兼顾整体准确率。
AURASeg: Attention-Guided Upsampling with Residual-Assisted Boundary Refinement for Drivable-Area Segmentation
- 用注意力引导逐级上采样融合语义与细节信息
- 引入残差边界精修模块，显著改善边缘定位
- 适合对边界精度要求高的自动驾驶场景
自由空间分割对于自主机器人识别可行驶区域并安全导航至关重要，涵盖室内、室外及道路场景。传统编码器-解码器模型常生成粗糙的区域掩码，丢失精确定位可行驶区域边界的细粒度空间信息。本文提出AURASeg框架，基于ResNet-18编码器，引入注意力渐进上采样解码器（APUD），逐步融合语义上下文与高分辨率空间细节；同时设计残差边界精修模块（RBRM），在最终预测前显式优化轮廓敏感特征。我们在室内仿真、地面机器人图像和道路驾驶基准上评估该模型。结果表明，本模型在区域级指标上保持与现有模型相当的竞争力，且在边界定位性能上尤为突出，甚至优于专门针对边界的先进方法。详细消融实验证明了所提解码与精修模块的有效性。
原文摘要 · Abstract (English)
Free-space segmentation is essential for autonomous robots to identify drivable regions and navigate safely across indoor, outdoor, and road-scene environments. However, conventional encoder-decoder models often recover coarse region masks while losing the fine spatial information needed to localize drivable-area boundaries accurately. We propose Attention-Guided Upsampling with Residual-Assisted Boundary Refinement (AURASeg), a segmentation framework designed to preserve region-level accuracy while improving boundary quality. Built on a ResNet-18 encoder, AURASeg introduces an Attention Progressive Upsampling Decoder (APUD) that progressively combines semantic context with high-resolution spatial detail, together with a Residual Boundary Refinement Module (RBRM) that explicitly refines contour-sensitive features before final prediction. We evaluate AURASeg across indoor simulation, ground-robot imagery, and road-driving benchmarks. The results show that our proposed model remains competitive with established segmentation models on region-level metrics while providing particularly strong boundary localization, including in comparison with boundary-focused methods. Detailed ablations further demonstrate the role of the proposed decoding and refinement modules.
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