Resource-aware On-device Deep Learning for Supermarket Hazard Detection
M. G. Sarwar Murshed, James J. Carroll, Nazar Khan, Faraz S. Hussain · 2020
Supermarkets need to implement safety measures to create a safe environment for shoppers and employees. Many of these injuries, such as falls, are caused by a lack of safety precautions. Such incidents are preventable by timely detection of hazardous conditions such as undesirable objects on supermarket floors. In this paper, we describe EdgeLite, a new lightweight deep learning model specifically designed for local and fast inference on edge devices which have limited memory and compute power. We show how EdgeLite was deployed on three different edge devices for detecting hazards in images of supermarket floors. On our dataset of supermarket floor hazards, EdgeLite outperformed six state-of-the-art object detection models in terms of accuracy when deployed on the three small devices. Our experiments also showed that energy consumption, memory usage, and inference time of EdgeLite were comparable to that of the baseline models. Based on our experiments, we provide recommendations to practitioners for overcoming resource limitations and execution bottlenecks when deploying deep learning models in settings involving resource-constrained hardware.