复杂环境下课堂多人状态检测算法研究  

Research on multi-person detection algorithm in classroom in complex environment

作　　者：冯文宇 张宇豪 张堃[1] 费敏锐 徐胜[4,5] Feng Wenyu;Zhang Yuhao;Zhang Kun;Fei Minrui;Xu Sheng(School of Electrical Engineering,Nantong University,Nantong 226007,China;School of Zhangjian,Nantong University,Nantong,China;Shanghai Key Laboratory of Power Station Automation Technology,School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 210053,China;School of Electronics and Information,Nantong Vocational University,Nantong 226007,China;The East China Science and Technology Research Institute of Changshu Co.,Ltd,Suzhou 215500,China)

机构地区：[1]南通大学电气工程学院,南通226007 [2]南通大学张謇学院,南通226007 [3]上海大学机电工程与自动化学院,上海市电站自动化技术重点实验室,上海210053 [4]南通职业大学电子信息工程学院,南通226007 [5]华东理工常熟研究院有限公司,苏州215500

出　　处：《电子测量与仪器学报》2021年第6期53-62,共10页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然基金重点项目(61633016);江苏省高校自然基金(18KJB510038);江苏省333工程可研项目(BRA2018218);国家级大学生创新创业训练计划资助项目(202010304065Z)资助。

摘　　要：新冠肺炎疫情背景下课堂多人佩戴口罩及姿态识别问题,提出了基于YOLO和OpenPose模型的课堂多人状态检测算法。提出的Efficient-YOLO模型,通过采用CBAM注意力模块、SPNET-NEW模块,解决了多人遮挡和无规则化目标的口罩佩戴检测精度问题。此外,提出了一种轻量化的Class-OpenPose模型检测学生上课姿态,该算法在OpenPose模型基础上,使用ShuffleNetV2-NEW对传统模型在底层特征提取方面进行改进,实现了复杂环境下关键姿态点的实时准确检测。实验表明,在课堂多人异常状态检测任务中,Class-OpenPose模型平均准确率高于传统模型,为79.0%,检测速度达到13.5 F/s;Efficient-YOLO口罩识别模型达到83.1%的平均准确率,检测时间仅需31.54 ms,为课堂学生状态检测提供了不错的算法思路。Aiming at the problem of multi-person wearing masks in the classroom and gesture recognition in COVID-19, this paper presents a multi-person state detection algorithm, based on the YOLO and OpenPose models. The Efficient-YOLO model proposed in this paper uses the classical CBAM attention and SPNET-NEW modules to deal with the problems of multi-person occlusion and irregular targets. In addition, this paper presents a lightweight Class-OpenPose model to detect the students’ posture. Based on the OpenPose model, our proposed algorithm uses ShuffleNetV2-NEW to improve the traditional model in terms of low-level feature extraction, and extracts correct key posture points in complex environments and in real-time. Experiments show that in the multi-person abnormal event detection task, the average accuracy of the Class-OpenPose model is 79.0% that is higher than that of the traditional model, and the detection speed reaches 13.5 F/s;the Efficient-YOLO mask recognition model achieves an average accuracy of 83.1%, and the detection time is only 31.54 ms, which provides a good algorithm idea for classroom student status detection.

关 键 词：多人异常检测 姿态识别 口罩识别 YOLO模型 OpenPose模型 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TH89[自动化与计算机技术—控制科学与工程]