基于TinyYOLOv2的轻量化人流监控装置的设计与实现  

Design and Implementation of Lightweight Pedestrian Flow Monitoring Device Based on TinyYOLOv2

作　　者：逯雨辰 梅健强[1] LU Yuchen;MEI Jianqiang(School of Electronic Engineering,Tianjin University of Technology and Education,Tianjin 300222,China)

机构地区：[1]天津职业技术师范大学电子工程学院,天津300222

出　　处：《现代信息科技》2022年第15期81-83,共3页Modern Information Technology

基　　金：天津市教委科研计划项目(JWK1605)。

摘　　要：人流密度监控对于场所秩序以及在场公民的安全保障有十分重要的意义。基于TinyYOLOv2搭建人脸检测平台,通过K210系统级芯片实现了轻量化的人员统计与人流密度监控装置。静态数据库模拟和动态实景测试的结果表明,该装置可实现多目标检测,并在实景条件下达到96.5%的人员数量统计准确率,可在保证人流监控精度的前提下有效降低疫情交叉感染的风险。The monitoring of crowd density is of great significance to the order of the place and the safety of the citizens present.A face detection platform is built based on TinyYOLOv2,and a lightweight personnel statistics and crowd density monitoring device is realized through K210 system level chip.The results of static database simulation and dynamic real scene test show that the device can realize multi-target detection,and achieve a statistical accuracy of 96.5%in the number of people under real scene conditions,which can effectively reduce the risk of cross infection of the epidemic on the premise of ensuring the accuracy of pedestrian flow monitoring.

关 键 词：人工智能 深度学习 神经网络 目标检测 人流监控 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]