基于YOLOv5算法的电子围栏入侵检测技术
Intrusion Detection Technology of Electronic Fence Based on YOLOv5 Algorithm
刘雯旭 1李卓 1闫洪伟1
作者信息
- 1. 首钢京唐钢铁联合有限责任公司冷轧作业部,河北 唐山 063200
- 折叠
摘要
随着物联网技术的进步与发展,电子围栏技术在安防领域的应用越来越广泛.为了有效升级钢厂厂房内的生产安全防护与监督措施,基于YOLOv5 深度学习算法提出一种电子围栏入侵检测技术.该技术通过对行人数据集进行收集和训练,将厂房内关键区域设置为电子围栏检测区域,对目标人员进行实时检测,并在有人员进入围栏时及时发出警报.结果表明该技术对于是否有人员入侵的判断具有较高的准确性和实时性,能够有效加强厂房内的安防措施.
Abstract
In order to effectively upgrade the production safety protection and supervision measures in the steel plant workshop,this paper proposes an electronic fence intrusion detection technology based on the YOLOv5 deep learning al-gorithm.This technology collects and trains pedestrian datasets,sets the key areas as electronic fence detection areas,and performs real-time detection on target personnel,issuing alarms when personnel enter the fence.The results show that this technology has high accuracy and real-time judgment for whether there is personnel intrusion,and can effectively strength-en the security measures in the workshop.
关键词
深度学习/YOLOv5/智能安防/电子围栏Key words
deep learning/YOLOv5/intelligent security/electronic fence引用本文复制引用
出版年
2024
NETL
NSTL
万方数据