  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/.
