摘要
电网线路和设备上的异物搭挂缠绕容易造成线路间接触短路,严重威胁着电网的安全稳定运行。针对电网常见异物风筝、气球、鸟窝等,提出了一种基于Faster R-CNN的高精度目标检测及分类模型。针对某类型异物图片样本量不足的问题,设计数据增强方法有效扩充训练集数据,并对样本图片进行多尺度处理以提升模型的训练速度以及模型对不同尺度的适应性,所得模型在测试集上的准确率和召回率可分别达到93.12%、94.75%,提出经数据增强后的训练集训练出的基于Faster R-CNN的目标检测模型取得了较好的检测效果。
Foreign matters hanging on the power grid line and electrical equipment are easy to cause the contact short circuit between the lines which threatens the safety and stability of the power grid.In this paper,a high-precision object detection model based on Faster R-CNN was proposed for common foreign matters in power grid,such as kites,balloons,birds’nests.Aiming at the problem of insufficient sample size of pictures,the data augmentation method is designed to effectively expand the data of the training set.Multi-scale processing of sample images is also carried out to improve the training speed of the model and the adaptability of the model to different scales.The accuracy and recall rate of the model can reach 93.12%and 94.75%respectively.It is proved that the target detection model based on Faster R-CNN trained by the training set with data enhancement has achieved better detection results.
作者
朱洪志
孙震
兰巧倩
沈亮熠
张冲
贺兴
ZHU Hongzhi;SUN Zhen;LAN Qiaoqian;SHEN Liangyi;ZHANG Chong;HE Xing(State Grid Shanghai Electric Power Company Jiading Power Supply Company,Shanghai 201800,China;Shanghai Jiao Tong University,Shanghai 201100,China)
出处
《电器与能效管理技术》
2021年第1期58-63,共6页
Electrical & Energy Management Technology
基金
国家自然科学基金青年科学基金项目(51907121)。
关键词
电网异物
卷积神经网络
数据增强
目标检测
foreign matters
convolutional neural network
data augmentation
object detection
