摘要
在大型工业厂房中,由于设备控制开关种类繁多、数量庞大,在日常的运维过程中,操作规程的繁杂性和人为判断的主观性可能导致操作失误,造成严重后果.为辅助操作人员准确判断设备开关状态是否正确,提出了面向设备开关状态识别的改进Faster R-CNN.首先,使用膨胀残差网络作为特征提取网络,在ResNet50中引入多分支膨胀卷积,融合不同感受野的信息;其次,改进特征金字塔网络,在原网络上增加一条自底向上的特征增强分支,融合多尺度的特征信息;然后,使用K-means++算法对开关边界框聚类,设计适合设备开关的候选框尺寸;最后,使用Soft-NMS代替非极大值抑制算法NMS来降低开关重叠对检测效果的影响,增强抑制重叠候选框的能力.在开关状态数据集上,改进Faster R-CNN的均值平均精度(mAP)达到了91.5%,并且已实际应用于抽水蓄能电站日常运维的设备开关状态辅助识别,满足复杂场景下的智能监管需求.
In large industrial plants,due to a wide variety and a large number of equipment control switches,the complexity of operating procedures and the subjectivity of human judgment may lead to operational errors and cause serious consequences in the daily operation and maintenance process.To assist operators in accurately judging whether the state of an equipment switch is correct,an improved Faster R-CNN algorithm is proposed for state recognition of equipment switches.Firstly,the dilated residual network(ResNet)is used as the feature extraction network,and the multibranch dilated convolution is introduced into ResNet50 to fuse the information of different receptive fields.Secondly,the feature pyramid network is improved by the addition of a bottom-up feature enhancement branch to the original network,which is used to integrate multi-scale feature information.Then,the K-means++algorithm is applied to cluster bounding boxes of switches,and the size of proposals for equipment switches is designed.Finally,the non-maximum suppression(NMS)algorithm is replaced with Soft-NMS to reduce the influence of switch overlap on the detection effect and enhance the performance of suppressing the overlapping proposals.On a switch state dataset,the mean average precision(mAP)of the improved Faster R-CNN reaches 91.5%.Moreover,it has been applied to assist state recognition of equipment switches in the daily operation and maintenance of pumped-storage power stations to meet the needs of intelligent supervision in complex scenarios.
作者
宋旭峰
蒋梦姣
周怡伶
吉俊杰
陆晓翔
SONG Xu-Feng;JIANG Meng-Jiao;ZHOU Yi-Ling;JI Jun-Jie;LU Xiao-Xiang(State Grid Xinyuan Holdings Co.Ltd.,Beijing 100032,China;East China Yixing Pumped Storage Power Co.Ltd.,Yixing 214200,China;College of Computer and Information,Hohai University,Nanjing 211100,China)
出处
《计算机系统应用》
2022年第10期211-224,共14页
Computer Systems & Applications
基金
国网新源控股有限公司科技项目(SGXY2000074)
作者简介
通信作者:陆晓翔,E-mail:luxx0824@163.com
