  Supermarkets need to ensure clean and safe environments for both shoppers and
employees. Slips, trips, and falls can result in injuries that have a physical
as well as financial cost. Timely detection of hazardous conditions such as
spilled liquids or fallen items on supermarket floors can reduce the chances of
serious injuries. This paper presents EdgeLite, a novel, lightweight deep
learning model for easy deployment and inference on resource-constrained
devices. We describe the use of EdgeLite on two edge devices for detecting
supermarket floor hazards. On a hazard detection dataset that we developed,
EdgeLite, when deployed on edge devices, outperformed six state-of-the-art
object detection models in terms of accuracy while having comparable memory
usage and inference time.
