An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

Personal protective equipment(PPE)donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However,the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’PPE use in the field of healthcare.In this paper,we construct a dress codes dataset for medical staff under the epidemic.And based on this,we propose a PPE donning automatic detection approach using deep learning.With the participation of health care personnel,we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment,and an effective and robust dataset is constructed with a total of 5233 preprocessed images.Starting from the task’s dual requirements for speed and accuracy,we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety.Experimental results show that compared with three typical deeplearning-based detection models,our method achieves a relatively optimal balance while ensuring high detection accuracy(84.14%),with faster processing time(42.02 ms)after the average analysis of 17 classes of PPE donning situation.Overall,this research focuses on the automatic detection of worker safety protection for the first time in healthcare,which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

基于改进YOLOv5s的个人防护设备检测算法研究

由于施工现场环境复杂、遮挡物多,由无人机收集到的工人个人防护设备(包括安全帽、反光背心)图像具有目标小、检测难度大的特点,使用原YOLOv5s模型进行检测存在错检、漏检的问题。因此,文章提出了一种基于改进的YOLOv5s的个人防护设备检测算法,通过在YOLOv5s模型中引入动态稀疏注意力模块,使模型更关注有价值的区域,并对损失函数进行优化,将原YOLOv5s模型的CIoU损失函数替换为Focal-EIoU损失函数,减少损失函数的自由度提高模型的性能。实验采用无人机采集和网络检索获得的自建个人防护设备数据集来进行训练。实验结果表明,经过100次迭代训练后,改进算法对于个人防护设备检测的FPS提高了6.79%,mAP提高了9.59%。由此可见,文章所提出的改进算法有效降低了模型的误检率和漏检率,在个人防护设备的检测上表现出了良好的性能。

基于改进YOLOv5s的矿工防护装备检测模型研究

为解决井下矿工由于安全意识淡薄而不正确使用个人防护装备(PPE)的问题,提出一种实时检测矿工是否佩戴PPE的目标检测网络模型。首先,采用现场监控视频采集和数据爬取的方式收集数据,使用半监督学习方法标注图像并利用图像增强技术扩充数据,构建井下矿工PPE数据集;其次,基于深度学习的方法建立PPE检测模型,将YOLOv5s的主干网络替换为MobileOne来降低网络参数,提升检测速度;最后,在特征融合层中嵌入三维注意力机制SimAM,实现对复杂环境下的PPE检测。实验结果表明:改进的模型平均检测精度达到94.4%,在CPU端的检测速度达到67帧/s,可以满足井下实时检测的要求。

基于改进YOLOv7的防护用品穿戴检测

为缓解YOLOv7在检测个人防护用品时面临标签重写、标签分配不平衡和特征耦合等问题,提出一种基于改进YOLOv7的检测方法.首先去除YOLOv7的大尺度和中尺度输出层,以降低标签重写率,且保证输出层得到充分训练;其次将输出层的定位和分类解耦,避免不同任务的特征表示互相影响,并选择在边界框级别检测防护服,在关键点级别检测防护帽和防护手套;最后引入部分卷积,实现实时检测.为验证该方法的有效性,使用实验人员穿戴防护用品的图像数据对所提方法进行验证.结果表明,相比YOLOv7,该方法的精确率和召回率分别提高了4.1和4.5个百分点,FPS(Frames Per Second)提升了1.3帧,可满足实验室场景下的个人防护用品穿戴检测需求.

3D打印技术在新型冠状病毒肺炎防治物资中的应用进展

阐述了3D打印的步骤、特点与优势,介绍了3D打印在应用于新型冠状病毒肺炎防治的个人防护装备、病毒检测工具和临床救治设备等医疗物资打印中的研究进展,分析了3D打印技术构建防治医疗物资的优势与不足,指出了制定具体统一的打印和检测标准是该领域下一步的重点研究方向。

GB/T 29483-2013《机械电气安全检测人体存在的保护设备应用》标准解析

随着社会和工业的发展,使用检测人体存在的保护设备来保障从业人员的人身安全得到了广泛的应用。GB/T 29483-2013《机械电气安全检测人体存在的保护设备应用》标准的制定明确了检测人体存在的保护设备的选择方式和相关要求,对提高机械工业的安全水平有重要意义。通过对GB/T 29483-2013标准的主要内容进行解读和分析,以便标准使用者理解标准内容,规范化使用标准,为检测人体存在的保护设备的设计制造和配备选取提供了参考。

多作业场景下个体防护装备自动检测研究

为实现对企业作业现场人员个体防护装备(Personal Protective Equipment,PPE)正确穿戴的精准监管,采用YOLOv5模型和单阶段目标检测算法,设计并开发了一种辅助作业现场PPE自动检测与研判的管理系统。通过对某企业工作全流程的调研和分析,明确了存在遮挡物、密集多目标、远距离目标及光照不均匀4类典型的作业场景及检测环境特征。基于不同作业场景下的监控设备完成数据采集,并建立了4种PPE的数据集。通过YOLOv5目标检测模型的训练和测试,融合现有的监控平台,实现了作业过程人员PPE使用情况的动态、快速研判。研究表明,系统对目前企业典型作业场景PPE使用情况的检测平均精确度为99.5%,同时兼顾了检测速度和运行成本,有效提升了企业现场安全管理效能。

一种基于YOLO v4和DeepSort的施工作业人员安全装备检测方法

为了快速有效地识别施工人员是否完整且正确地佩戴安全装备,本文提出了一种基于YOLO v4算法和DeepSort算法的个人安全防护装备检测方法。首先,提出了一种基于统计的数据增强方法对获取的图像数据进行扩展,使训练模型具有更高的稳健性;其次,结合YOLO v4算法和DeepSort算法对人体和安全防护装备进行检测、跟踪;然后,分析人体框与安全防护装备框的IoU值和相对位置,判断每个工作人员是否完整且正确地佩戴安全装备;最后,显示未佩戴或佩戴错误的装备信息并进行报警。实验结果表明,本文中提出的方法平均检测精度较高,可满足工业应用的精确度要求,能够实现智能化、数字化的实时监控。