Classic computer vision algorithms, instance segmentation, and semantic
segmentation can not provide a holistic understanding of the surroundings for
the visually impaired. In this paper, we utilize panoptic segmentation to
assist the navigation of visually impaired people by offering both things and
stuff awareness in the proximity of the visually impaired efficiently. To this
end, we propose an efficient Attention module -- Lintention which can model
long-range interactions in linear time using linear space. Based on Lintention,
we then devise a novel panoptic segmentation model which we term Panoptic
Lintention Net. Experiments on the COCO dataset indicate that the Panoptic
Lintention Net raises the Panoptic Quality (PQ) from 39.39 to 41.42 with 4.6\%
performance gain while only requiring 10\% fewer GFLOPs and 25\% fewer
parameters in the semantic branch. Furthermore, a real-world test via our
designed compact wearable panoptic segmentation system, indicates that our
system based on the Panoptic Lintention Net accomplishes a relatively stable
and exceptionally remarkable panoptic segmentation in real-world scenes.

通过提出一种基于 Panoptic Lintention Net 的紧凑可穿戴全景分割系统，协助视障人士高效地获得环境中物品和场景的感知，使用 Lintention 注意模块快速建模长程关联，实现对现实场景的全景分割，获得了较稳定和显著的分割效果。