论文标题
使用深度学习边缘的超市中的危险检测
Hazard Detection in Supermarkets using Deep Learning on the Edge
论文作者
论文摘要
超市需要确保购物者和员工的清洁环境。滑倒，旅行和跌倒会导致具有身体和经济成本的伤害。及时检测危险状况，例如溢出的液体或超市地板上掉落的物品，可以减少严重伤害的机会。本文介绍了Edgelite，这是一种新颖，轻巧的深度学习模型，可轻松部署和推断资源约束设备。我们描述了在两个边缘设备上使用Edgelite来检测超市地板危险的使用。在部署在边缘设备上时，我们开发了Edgelite的危险检测数据集，以准确性优于六个最先进的对象检测模型，同时具有可比的内存使用情况和推理时间。
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.
