  The recent surge of automation in the retail industries has rapidly increased
demand for applying deep learning models on mobile devices. To make the deep
learning models real-time on-device, a compact efficient network becomes
inevitable. In this paper, we work on one such common problem in the retail
industries - Shelf segmentation. Shelf segmentation can be interpreted as a
pixel-wise classification problem, i.e., each pixel is classified as to whether
they belong to visible shelf edges or not. The aim is not just to segment shelf
edges, but also to deploy the model on mobile devices. As there is no standard
solution for such dense classification problem on mobile devices, we look at
semantic segmentation architectures which can be deployed on edge. We modify
low-footprint semantic segmentation architectures to perform shelf
segmentation. In addressing this issue, we modified the famous U-net
architecture in certain aspects to make it fit for on-devices without impacting
significant drop in accuracy and also with 15X fewer parameters. In this paper,
we proposed Light Weight Segmentation Network (LWSNet), a small compact model
able to run fast on devices with limited memory and can train with less amount
(~ 100 images) of labeled data.
