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A Mask Detection Method for Shoppers Under the Threat of COVID-19 Coronavirus

机译：Covid-19 Coronavirus威胁下购物者的掩模检测方法

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摘要

Object detection, which aims to automatically mark the coordinates of objects of interest in pictures or videos, is an extension of image classification. In recent years, it has been widely used in intelligent traffic management, intelligent monitoring systems, military object detection, and surgical instrument positioning in medical navigation surgery, etc. COVID-19, a novel coronavirus outbreak at the end of 2019, poses a serious threat to public health. Many countries require everyone to wear a mask in public to prevent the spread of coronavirus. To effectively prevent the spread of the coronavirus, we present an object detection method based on single-shot detector (SSD), which focuses on accurate and real-time face masks detection in the supermarket. We make contributions in the following three aspects: 1) presenting a lightweight backbone network for feature extraction, which based on SSD and spatial separable convolution, aiming to improve the detection speed and meet the requirements of real-time detection; 2) proposing a Feature Enhancement Module (FEM) to strengthen the deep features learned from CNN models, aiming to enhance the feature representation of the small objects; 3) constructing COVID-19Mask, a large-scale dataset to detect whether shoppers are wearing masks, by collecting images in two supermarkets. The experiment results illustrate the high detection precision and real-time performance of the proposed algorithm.
机译：目标检测，旨在自动标记图片或视频的感兴趣对象的坐标，是图像分类的扩展。近年来，它已广泛应用于智能交通管理，智能监测系统，军事对象检测和医疗航海手术中的手术器械定位等。Covid-19，2019年底的新型冠状病毒爆发，构成了严肃的对公共卫生的威胁。许多国家要求每个人都在公共场合中佩戴面具，以防止冠状病毒的传播。为了有效地防止冠状病毒的扩散，我们介绍了一种基于单次检测器（SSD）的物体检测方法，该方法侧重于超市中的准确和实时面部掩模。我们在以下三个方面做出贡献：1）提出了一种用于特征提取的轻质骨干网，其基于SSD和空间可分离卷积，旨在提高检测速度并满足实时检测的要求; 2）提出一种特征增强模块（FEM）以加强从CNN模型中学到的深度特征，旨在增强小物体的特征表示; 3）构造Covid-19Mask，一个大型数据集，以通过在两个超市中收集图像来检测购物者是否戴着面具。实验结果说明了所提出的算法的高检测精度和实时性能。

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