Precise and rapid delineation of sharp boundaries and robust semantics is essential for numerous downstream robotic tasks, such as robot grasping and manipulation, real-time semantic mapping, and online sensor calibration performed on edge computing units. Although boundary detection and semantic segmentation are complementary tasks, most studies focus on lightweight models for semantic segmentation but overlook the critical role of boundary detection. In this work, we introduce Mobile-Seed, a lightweight, dual-task framework tailored for simultaneous semantic segmentation and boundary detection. Our framework features a two-stream encoder, an active fusion decoder (AFD) and a dual-task regularization approach. The encoder is divided into two pathways: one captures category-aware semantic information, while the other discerns boundaries from multi-scale features. The AFD module dynamically adapts the fusion of semantic and boundary information by learning channel-wise relationships, allowing for precise weight assignment of each channel. Furthermore, we introduce a regularization loss to mitigate the conflicts in dual-task learning and deep diversity supervision. Compared to existing methods, the proposed Mobile-Seed offers a lightweight framework to simultaneously improve semantic segmentation performance and accurately locate object boundaries. Experiments on the Cityscapes dataset have shown that Mobile-Seed achieves notable improvement over the state-of-the-art (SOTA) baseline by 2.2 percentage points (pp) in mIoU and 4.2 pp in mF-score, while maintaining an online inference speed of 23.9 frames-per-second (FPS) with 1024x2048 resolution input on an RTX 2080 Ti GPU. Additional experiments on CamVid and PASCAL Context datasets confirm our method's generalizability. Code and additional results are publicly available at https://whu-usi3dv.github.io/Mobile-Seed/.

翻译：精确且高效地勾勒清晰边界与鲁棒语义对于众多下游机器人任务至关重要，例如在边缘计算单元上执行的机器人抓取与操作、实时语义建图以及在线传感器校准。尽管边界检测与语义分割是互补任务，但现有研究大多专注于语义分割的轻量化模型，而忽视了边界检测的关键作用。本文提出Mobile-Seed——一种专为同步语义分割与边界检测设计的轻量级双任务框架。该框架包含双流编码器、主动融合解码器（AFD）以及双任务正则化方法。编码器分为两条路径：一条捕获类别感知的语义信息，另一条从多尺度特征中识别边界。AFD模块通过学习通道间关系动态调整语义与边界信息的融合，从而实现每个通道的精确权重分配。此外，我们引入正则化损失以缓解双任务学习中的冲突问题及深度多样性监督。与现有方法相比，Mobile-Seed提供轻量级框架，在提升语义分割性能的同时准确定位对象边界。在Cityscapes数据集上的实验表明，Mobile-Seed相比现有最优（SOTA）基线方法在mIoU上提升2.2个百分点，mF-score上提升4.2个百分点，且在RTX 2080 Ti GPU上对1024×2048分辨率输入保持23.9帧/秒（FPS）的在线推理速度。在CamVid与PASCAL Context数据集上的额外实验进一步验证了方法的泛化能力。代码与更多结果已公开于https://whu-usi3dv.github.io/Mobile-Seed/。