[1]袁　杰,万忠原,加尔肯别克,等.基于RT-GLV的变电站电力人员绝缘手套穿戴检测方法[J].郑州大学学报(工学版),2026,47(01):25-32.[doi:10.13705/j.issn.1671-6833.2026.01.001]
　YUAN Jie,WAN Zhongyuan,JIA Erkenbieke,et al.A Detection Method for Insulating Gloves Wearing of Power Personnel in Substations Based on RT-GLV[J].Journal of Zhengzhou University (Engineering Science),2026,47(01):25-32.[doi:10.13705/j.issn.1671-6833.2026.01.001]
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基于RT-GLV的变电站电力人员绝缘手套穿戴检测方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年01期
页码:
25-32
栏目:
出版日期:
2026-01-06

文章信息/Info

Title:
A Detection Method for Insulating Gloves Wearing of Power Personnel in Substations Based on RT-GLV
文章编号:
1671-6833(2026)01-0025-08
作者:
袁　杰1;  万忠原2;  加尔肯别克1;  杨怡程2;  祁鹏程2;  陈治润2
1.新疆大学 智能科学与技术学院,新疆 乌鲁木齐 830017;2.新疆大学 电气工程学院,新疆 乌鲁木齐 830017
Author(s):
YUAN Jie1;  WAN Zhongyuan2;  JIA Erkenbieke1;  YANG Yicheng2;  QI Pengcheng2;  CHEN Zhirun2
1.School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China; 2.School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
关键词:
绝缘手套;  RT-DETR;  多尺度融合;  轻量化;  Powerful-IoU
Keywords:
insulating gloves;  RT-DETR;  multi-scale fusion;  lightweight;  Powerful-IoU
分类号:
TP391.4TM93
DOI:
10.13705/j.issn.1671-6833.2026.01.001
文献标志码:
A
摘要:
变电站电力人员作业穿戴的绝缘手套有目标小、易遮挡的特点,而一般的特征融合网络往往会丢失小目标信息。针对此问题,构建一种多尺度小目标特征融合网络STPFM,对RT-DETR-R18模型进行改进,设计了电力人员绝缘手套穿戴模型RT-GLV。首先,用STPFM网络代替CCFM网络,利用STPFM网络的SSFF模块、TFE模块融合多尺度特征信息,此外,增加一个以SSFF模块为核心的小目标检测层,增强模型对小目标信息的学习能力;其次,为解决替换的STPFM网络模型参数量过大的问题,构建一种轻量化PB_Block模块,只替换主干网络中包含小目标信息较少的P4、P5层的模块,在轻量化模型的同时,又降低小目标信息的损失;最后,采用PIoUv2损失函数增强模型对难易样本的学习能力。实验结果表明:RT-GLV模型在电力人员绝缘手套穿戴检测中表现优异,与RT-DETR-R18相比,mAP@0.5提高2.1百分点,F1分数提高1.6百分点,参数量减少21.5%;在小目标检测方面,穿戴绝缘手套的AP@0.5提高1.4百分点,未穿戴绝缘手套的AP@0.5提高6.4百分点,且模型检测速度达到91.3帧/s,满足电力人员绝缘手套穿戴检测的准确性、实时性要求。
Abstract:
The insulating gloves worn by power personnel in substations were small target in size and were easily obscured. Aiming at the problem that general feature fusion networks often lost small target information, a multiscale small target feature fusion network named STPFM was constructed. The RT-DETR-R18 model was improved, and the RT-GLV model was designed for detecting whether power personnel were wearing insulating gloves. Firstly, the STPFM network was used to replace the CCFM network. The SSFF module and TFE module of the network were utilized to fuse multi-scale feature information. In addition, a small target detection layer with the SSFF module as the core was added to enhance the model′s ability to learn small target information. Secondly, to address the issue of excessive model parameters after replacing the STPFM network, a lightweight PB_Block module was constructed. Only the modules in the P4 and P5 layers of the Backbone network, which contained less small target information, were replaced. It not only lightened the model but also reduced the loss of small target information. Finally, the PIoUv2 loss function was adopted to enhance the model′s learning ability for both easy and difficult samples. The experimental results showed that the RT-GLV model performed excellently in the detection of whether power personnel were wearing insulating gloves. Compared with the RT-DETR-R18, the mAP@0.5 was increased by 2.1 percentage points, the F1 score was increased by 1.6 percentage points, and the number of model parameters was reduced by 21.5%. In terms of small target detection, the AP@0.5 of wearing insulating gloves was increased by 1.4 percentage points, and the AP@0.5 of not wearing insulating gloves was increased by 6.4 percentage points. Moreover, the model′s detection speed reached 91.3 frame per second, meeting the requirements of accuracy and realtime performance for detecting whether power personnel were wearing insulating gloves.

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更新日期/Last Update: 2026-01-17