摘要
为了解决当前电力建筑工地信息化程度低、智能化管理薄弱、监管区域较小等问题,提出基于目标关联性算法的电力智慧工地区域监测方法。针对现有方法中待测目标(人、车)定位过程繁琐,以及无法充分利用环境相关信息等问题,引入关联性融合模块,提升待测目标相对位置的信息利用率,并通过同一网络模型进行多目标定位,在有效提升定位效率的同时,降低定位的复杂度。利用工地实拍图像进行实验,结果表明所提方法对工地区域中待测目标的检测准确率达99.2%,工程应用价值较高。
In view of electric power construction site with low level of informationization,lack of intelligent management,and small supervision areas,the paper proposes a smart monitoring method for electric power construction site areas based on target correlation method. Given the cumbersome positioning process of the targets to be measured(person,car)in the existing method and the inability to fully utilize the environment-related information,the correlation fusion module is introduced to improve the information utilization rate of the relative position of the targets to be tested. Multi-target positioning is carried out using the same network model to reduce the complexity of positioning and improve positioning efficiency. Experiments are carried out using real images of the construction site.The results show that the detection accuracy of the proposed method for the target to be measured in the construction site area is up to 99.2%,which is of high engineering application value.
作者
陈畅
刘鉴栋
闫云凤
齐冬莲
CHEN Chang;LIU Jiandong;YAN Yunfeng;QI Donglian(Guangzhou Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Guangzhou 510050,China;College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China)
出处
《浙江电力》
2022年第10期11-15,共5页
Zhejiang Electric Power
关键词
智慧工地
区域监测
目标关联性
深度学习
smart construction site
area monitoring
target correlation
deep learning
