Hazard Detection in Supermarkets using Deep Learning on the Edge

• DOI: 10.48550/arxiv.2003.04116
• Publication Date: 2020-01-01
• Authors: Murshed, M. G. Sa...; Verenich, Edward; Carroll, James J.; Khan, Nazar; Hussain, Faraz
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; Machine Learning (stat.ML); 02 engineering and technology; Electrical Engineering and Systems Science - Image and Video Processing; Machine Learning (cs.LG); Statistics - Machine Learning; 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering

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.<br/>6 pages, conference<br/>