摘要
采用X射线数字成像的方式能够实现对电力电缆本体的无损检测,但目前缺乏对X射线数字影像的深度处理和缺陷识别方法,无法从原始的数字影像中直接对电缆本体和缺陷进行检测识别。因此,本文研究了电力电缆X射线数字影像深度处理和缓冲层缺陷智能识别技术,提出了全卷积神经网络(full convolution neural network,FCN)法。采用灰度处理技术,将原始的图像灰阶范围压缩至人眼可识别范围,然后进行缺陷标识,再采用传统卷积神经网络(convolution neural network,CNN)法和所提方法对图像数据进行训练,实现对电力电缆缓冲层缺陷的智能识别。结果表明,相比于CNN法,所提FCN法具有更加清晰直观的识别效果。
The nondestructive detection of power cable body can be realized by X-ray digital imaging,but there is no depth processing and defect recognition method for X-ray digital images at present.The cable body and defects can not be detected and identified directly from the original digital image.Therefore,this paper studies the advanced processing method of power cable X-ray digital image and the intelligent identification technology of buffer layer defect,and puts forward the full convolution neural network(FCN)method.By using gray level processing technology,the original image gray scale range is compressed to the human eye recognizable range,then the defect identification is carried out,and then the image data is trained by the traditional convolution neural network(CNN)and the proposed method.The intelligent recognition of power cable buffer layer defects is realized.Compared with the traditional CNN,the proposed FCN has more clear and intuitive recognition effect.
作者
刘三伟
谢亿
张军
段建家
黄福勇
段肖力
曾泽宇
LIU Sanwei;XIE Yi;ZHANG Jun;DUAN Jianjia;HUANG Fuyong;DUAN Xiaoli;ZENG Zeyu(Electric Power Research Institute of State Grid Hunan Electric Power Co.,Ltd.,Changsha 41007,China)
出处
《南方电网技术》
CSCD
北大核心
2020年第12期66-70,共5页
Southern Power System Technology
基金
国网湖南电力有限公司资助项目(5216A5200004)。
关键词
电力电缆
缓冲层缺陷
X射线数字影像
深度处理
缺陷智能识别
power cable
buffer layer defect
X-ray digital image
depth processing
intelligent defect identification
