结合场景分析的输电线路通道可视化分级预警研究

doi:10.11985/2022.03.023

摘要/Abstract

摘要：

针对输电线路横跨地域广,输电通道中隐患目标多的问题,提出了输电线路通道可视化分级预警模型。首先改进深度残差网络提取输入图像的多光谱信息,通过软阈值化来减少噪声影响,提高输电线路通道场景分析模型的准确度;然后利用YOLOv3目标检测算法构建输电线路通道隐患目标识别模型,针对隐患中的烟雾、施工车辆目标小的问题,采用难负样本挖掘策略,减少图片背景的影响,再根据输电线路通道的分级预警结构构建分级预警模型。研究结果表明,结合场景分析的输电线路通道可视化分级预警模型能够科学、准确地反映出输电线路通道的隐患预警状态,为输电线路运行维护工作提供指导。

关键词: 输电线路通道可视化, 场景分析, 目标识别, 分级预警, 深度学习

Abstract:

In view of the problem that the transmission line spans a wide area and has many hidden targets in the transmission channel, the visual grading warning model of the transmission line channel is put forward. First of all, the depth residual network to is improved to extract the multispectral information of the input image, the noise effect is reduced by soft threshold, the accuracy of the transmission line channel scene analysis model is improved, and then YOLOv3 target detection algorithm is used to build the transmission line channel hidden danger target identification model. Aiming at the problems of hidden smoke and small targets of contruction vihicles, hard example mining strategy is adopted to reduce the impact of the picture background, and then an early warning model is build based on the transmission line channel classification early warning structure. The results show that the visual and graded early warning model of transmission line channel combined with scene analysis can scientifically and accurately reflect the hidden danger warning state of transmission line channel and provide guidance for the operation and maintenance of transmission line.

Key words: Transmission line channel visualization, scene analysis, target recognition, hierarchical warning, deep learning

中图分类号:

TM391

武永泉, 张四维, 彭冲, 焦良葆, 浦东. 结合场景分析的输电线路通道可视化分级预警研究[J]. 电气工程学报, 2022, 17(3): 194-202.

WU Yongquan, ZHANG Siwei, PENG Chong, JIAO Liangbao, PU Dong. Research on Visual Hierarchical Early Warning of Transmission Line Channel Combined with Scene Analysis[J]. Journal of Electrical Engineering, 2022, 17(3): 194-202.

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场景类别	隐患目标类别	分级预警库
任意场景	烟雾&山火	一级+红色信号①
任意场景	异物(风筝、损坏塑料大棚、损坏布艺广告牌等)	一级+红色信号②
三跨(高速铁路、高速公路、河流)	任意隐患	一级+红色信号③
固定施工-隐患高发段	高危工程机械(吊车、混凝土浇灌车)	一级+红色信号④
高茎植物监控场景	高危&常规工程机械	二级+黄色信号⑤
固定施工-隐患低发段	高危工程机械	二级+黄色信号⑥
固定施工-隐患低发段	常规工程机械(混凝土搅拌车、挖掘机、铲车)	三级+蓝色信号⑦

序号	隐患目标的定义
①	烟雾&山火
②	异物(风筝、损坏塑料大棚、损坏布艺广告牌等)
③	高危工程机械(吊车、混凝土浇灌车)
④	常规工程机械(混凝土搅拌车、挖掘机、铲车)

序号	场景类别的定义
①	三跨(高速铁路、高速公路、河流)
②	固定施工-隐患高发段
③	高茎植物监控场景
④	固定施工-隐患低发段

隐患目标类别	标记类别名称	数据量
烟雾	smoke	750
山火	wildfire	750
异物	foreign-matter	1 500
吊车	crane	3 600
混凝土浇灌车	concrete-pour-truck	500
混凝土搅拌车	concrete-mixer-truck	500
挖掘机	excavator	4 600
铲车	forklift	1 000

原YOLOv3目标检测网络			加入难负样本挖掘策略
隐患目标类别	查全率	查准率	隐患目标类别	查全率	查准率
烟雾	0.831	0.852	烟雾	0.853	0.922
山火	0.923	0.922	山火	0.942	0.965
异物	0.803	0.723	异物	0.806	0.731
吊车	0.872	0.885	吊车	0.914	0.903
混凝土浇灌车	0.891	0.858	混凝土浇灌车	0.926	0.873
混凝土搅拌车	0.908	0.839	混凝土搅拌车	0.956	0.862
挖掘机	0.912	0.901	挖掘机	0.934	0.905
铲车	0.781	0.752	铲车	0.783	0.737