[1]吴奇伟,薛 海,王 真,等.基于轻量化深度学习驱动的输电线路异物入侵实时监测方法[J].机械与电子,2025,(12):38-44.
　WU Qiwe,XUE Hai,WANG Zhen,et al.A Real-time Monitoring Method for Foreign Object Intrusion in Transmission Lines Driven by Lightweight Deep Learning[J].Machinery & Electronics,2025,(12):38-44.
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基于轻量化深度学习驱动的输电线路异物入侵实时监测方法()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年12期
页码:
38-44
栏目:
自动控制与检测
出版日期:
2025-12-23

文章信息/Info

Title:
A Real-time Monitoring Method for Foreign Object Intrusion in Transmission Lines Driven by Lightweight Deep Learning
文章编号:
1001-2257 ( 2025 ) 12-0038-07
作者:
吴奇伟 1 ; 薛 海 2 ; 王 真 2 ; 刘征宇 2 ; 胡妍捷 2
1. 国网江苏省电力有限公司,江苏 南京 210000 ;
2. 国网江苏省电力有限公司电力科学研究院,江苏 南京 211103
Author(s):
WU Qiwe1 ;  XUE Hai2 ;  WANG Zhen2 ;  LIU Zhengyu2 ;  HU Yanjie2
( 1.State Grid Jiangsu Electric Power Co. , Ltd. , Nanjing 210000 , China ;
2.Electric Power Research Institute , State Grid Jiangsu Electric Power Co. , Ltd. , Nanjing 211103 , China )
关键词:
异物检测; 输电线路;  YOLOv7-seg ; 边缘设备; ConvNeXt
Keywords:
foreign object detection ;  transmission lines ;  YOLOv7-seg ;  edge devices ;  ConvNeXt
分类号:
TM755 ;TP391
文献标志码:
A
摘要:
针对输电线路异物入侵难以在线可靠检测与跟踪的问题,提出一种基于轻量化深度学习驱动的实时监测方法,以克服现有算法计算复杂度高、难以适应现场资源受限环境的不足。首先,提出一个基于类别聚合的 YOLOv7-seg 异物分割模型,用于从复杂背景中识别并裁剪出异物。其次,利用三元组损失函数训练 ConvNeXt 模型,使其能对剪切图像进行特征提取,并利用该模型和训练数据生成标准特征数据库。然后,提出一种新型的基于特征辅助的 IoU 多目标跟踪算法,确保其在小算力硬件上具备更强的场景适应能力。最后,提出异物实时跟踪监测的框架,并在边缘设备上部署进行实验验证。实验结果表明,所提方法可以部署到低成本的边缘设备上,具有良好的适应性和鲁棒性。
Abstract:
To address the challenge of reliable online detection and tracking of foreign object intrusions on transmission lines , a lightweight deep learning driven method of real-time monitoring is proposed.It overcomes the high computational complexity of existing algorithms and their incompatibility with resource limited field environments.Firstly , this paper proposes a YOLOv7-seg foreign object image segmentation model based on category aggregation for identifying and cropping foreign objects from complex backgrounds.Secondly , the ConvNeXt model is trained using the triple loss function to enable it to extract features from clipped images , and a standard feature database is generated by using this model and the training data.Then , this paper proposes a novel feature assisted IoU multi-object tracking algorithm to ensure stronger scene adaptability on hardware with low computing power.Finally , a framework for real time tracking and detection of foreign objects is proposed and deployed on edge devices for experimental verification.The experimental results show that the method proposed in this paper can be deployed on low-cost edge devices and has good adaptability and robustness.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2025-08-10
基金项目:国家电网科技项目资助( J2024120 )
作者简介:吴奇伟 ( 1992- ),男,江苏无锡人,博士,高级工程师,研究方向为电力巡检。
更新日期/Last Update: 2026-01-04