计算机与数字工程2024，Vol.52Issue(4)：1005-1013,9.DOI:10.3969/j.issn.1672-9722.2024.04.009
基于轻量级YOLOv3的员工违规行为检测算法
A Staff Violation Detection Algorithm Based on Lightweight YOLOv3
王纵驰 1刘健 2王培 2雷磊 3于佳耕 4陶青川3
作者信息
- 1. 中国航空油料集团有限公司 北京 100088
- 2. 航天神舟智慧系统技术有限公司 北京 100029
- 3. 四川大学电子信息学院 成都 610065
- 4. 中国科学院软件研究所 北京 100190
- 折叠
摘要
Abstract
With industrial Internet security receiving more and more attention,how to ensure information security manage-ment through technical means has become a question for unit managers to consider.In this paper,a lightweight convolutional net-work-based algorithm for staff violation detection is proposed for the security needs of similar scenarios such as duty rooms,central control rooms and management rooms.The algorithm is able to monitor the behavioural status of staff in designated areas in real time,avoiding malicious incidents such as production accidents in equipment and facilities and leakage of equipment information that may occur when staff are away from work or sleeping for long periods of time.The violation detection algorithm consists of two parts,which are the human detection and the behaviour recognition algorithm.Firstly,the human detection frame is obtained through a lightweight human detection network.The human detection frame is then identified using a target tracking algorithm and a behaviour recognition algorithm to determine if there is a breach of the rules by the staff member.Experimental data shows that the algorithm significantly reduces network weights as well as computational effort,with detection speeds of up to 13 ms on edge devices and violation detection accuracies of up to 96.6%on real-world scenario datasets.关键词
违规行为检测/YOLOv3/人体检测/边缘设备Key words
violation detection/YOLOv3/human detection/edge device分类
信息技术与安全科学引用本文复制引用
王纵驰,刘健,王培,雷磊,于佳耕,陶青川..基于轻量级YOLOv3的员工违规行为检测算法[J].计算机与数字工程,2024,52(4):1005-1013,9.
