  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.
