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基于广义交并比的电力梯子自动化监测方法

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针对传统电力监控存在效率低下、防控不全面和安全风险高等问题,设计了一种基于广义交并比的电力梯子自动化监测方法.首先,从大量监控视频中抽帧处理并制作一组电力梯子检测数据集,用于训练和测试算法.然后,在保证检测精度的前提下,使用MobileNetv2的主干网络替代YOLOv3的主干网络,适度地降低计算冗余度.最后,利用广义交并比损失提升检测精度.结果表明,与现有同类网络相比,提出的检测网络在电力场景梯子检测数据集上的检测精度为93.1%,具有更高的检测精度和速度,适合实际工程应用.
Automated Monitoring Method for Power Ladders Based on the Generalized Intersection over Union
Aiming at the problems of low efficiency,incomplete prevention and control,and high safety risks in traditional power monitoring,a power ladder automation monitoring method based on the generalized intersection over union was proposed.Firstly,a set of power ladder detection datasets was extracted and processed from a large number of surveillance videos for training and testing algorithms;then,while ensuring detection accuracy,MobileNetv2's backbone network was used to replace YOLOv3's backbone network,moderately reducing computational redundancy;finally,the generalized intersection and union ratio loss was employed to improve detection accuracy.The results show that compared with existing similar networks,the proposed detection network has the detection accuracy of 93.1%on the power scene ladder detection dataset,which has higher detection accuracy and speed and is suitable for practical engineering applications.

object detectionpower laddersdeep learningfeature extractionfeature fusion

朱建宝、桑顺、俞鑫春、马青山、张斌

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国网江苏省电力有限公司南通供电分公司,江苏南通 226006

上海交通大学电力传输与功率变换控制教育部重点实验室,上海 200240

江苏奥威信息系统工程有限公司,江苏南通 226007

目标检测 电力梯子 深度学习 特征提取 特征融合

电力传输功率变换控制教育部重点实验室开放课题国网江苏省电力有限公司科技项目

2021AC03J2020054

2024

电气自动化
上海电气自动化设计研究所有限公司 上海市自动化学会

电气自动化

CSTPCD
影响因子:0.377
ISSN:1000-3886
年,卷(期):2024.46(4)