基于Faster R-CNN和IoU优化的实验室人数统计与管理系统

针对人员位置相对固定的场景中实时人数统计的管理需求,以普通高校实验室为例,设计并实现了一套基于更快速的区域卷积神经网络(Faster R-CNN)和交并比(IoU)优化的实验室人数统计与管理系统。首先,使用Faster R-CNN模型对实验室内人员头部进行检测;然后,根据模型检测的输出结果,利用IoU算法滤去重复检测的目标;最后,采用基于坐标定位的方法确定实验室内各个工作台是否有人,并将相对应的数据存入数据库。该系统主要功能有:①实验室实时视频监控及远程管理;②定时自动拍照检测采集数据,为实验室的量化管理提供数据支撑;③实验室人员变化数据查询与可视化展示。实验结果表明,所提基于Faster R-CNN和IoU优化的实验室人数统计与管理系统可用于办公场景中实时人数统计和远程管理。

Laboratory personnel statistics and management system based on Faster R-CNN and IoU optimization

Aiming at the management requirement of real-time personnel statistics in office scenes with relatively fixed personnel positions, a laboratory personnel statistics and management system based on Faster Region-based Convolutional Neural Network (Faster R-CNN) and Intersection over Union (IoU) optimization was designed and implemented with an ordinary university laboratory as the example. Firstly, Faster R-CNN model was used to detect the heads of the people in the laboratory. Then, according to the output results of the model detection, the repeatedly detected targets were filtered by using IoU algorithm. Finally, a coordinate-based method was used to determine whether there were people at each workbench in the laboratory and store the corresponding data in the database. The main functions of the system are as follows:① real-time video surveillance and remote management of the laboratory; ② timed automatic photo, detection and acquisition of data to provide data support for the quantitative management of the laboratory; ③ laboratory personnel change data query and visualization. The experimental results show that the proposed laboratory personnel statistics and management system based on Faster R-CNN and IoU optimization can be used for real-time personnel statistics and remote management in office scenes.