基于目标关联性的电力智慧工地区域监测方法A smart monitoring method for electric power construction site area based on target correlation
陈畅,刘鉴栋,闫云凤,齐冬莲
CHEN Chang,LIU Jiandong,YAN Yunfeng,QI Donglian
摘要(Abstract):
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.
关键词(KeyWords):
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
基金项目(Foundation):
作者(Author):
陈畅,刘鉴栋,闫云凤,齐冬莲
CHEN Chang,LIU Jiandong,YAN Yunfeng,QI Donglian
DOI: 10.19585/j.zjdl.202210002
参考文献(References):
- [1]李霞,吴跃明.物联网+下的智慧工地项目发展探索[J].建筑安全,2017,32(2):35-39.
- [2]朱贺,张军,宁文忠,等.智慧工地应用探索——智能化建造、智慧型管理[J].中国建设信息化,2017(9):76-78.
- [3]徐守坤,王雅如,顾玉宛,等.基于改进Faster RCNN的安全帽佩戴检测研究[J].计算机应用研究,2020,37(3):901-905.
- [4]张明媛,曹志颖,赵雪峰,等.基于深度学习的建筑工人安全帽佩戴识别研究[J].安全与环境学报,2019,19(2):535-541.
- [5]施辉,陈先桥,杨英.改进YOLO v3的安全帽佩戴检测方法[J].计算机工程与应用,2019,55(11):213-220.
- [6]邓天民,冒国韬,周臻浩,等.基于密集连接卷积神经网络的道路车辆检测与识别算法[J].计算机应用,2022,42(3):883-889.
- [7]宋焕生,张向清,郑宝峰,等.基于深度学习方法的复杂场景下车辆目标检测[J].计算机应用研究,2018,35(4):1270-1273.
- [8] XIAO B,WU H,WEI Y. Simple baselines for human pose estimation and tracking[C]//European Conference on Computer Vision(ECCV). Munich:Amazon etc.,2018.
- [9] WANG J,SUN K,CHENG T,et al.Deep high-resolution representation learning for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,43(10):3349-3364.
- [10] SUN K,XIAO B,LIU D,et al.Deep high-resolution representation learning for human pose estimation[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR),Los Angeles:Amazon etc.,2019.