[1]冯振新,许晓路,周东国,等.基于Canny算子的简化PCNN电力故障区域提取方法[J].红外技术,2019,41(7):634-639.[doi:10.11846/j.issn.1001_8891.201907007]
 FENG Zhengxin,XU Xiaolu,ZHOU Dongguo,et al.Fault Region Extraction of Electrical Equipments in Infrared Images by Pulse-coupled Neural Network Method with Canny Operator[J].Infrared Technology,2019,41(7):634-639.[doi:10.11846/j.issn.1001_8891.201907007]
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基于Canny算子的简化PCNN电力故障区域提取方法
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
41卷
期数:
2019年第7期
页码:
634-639
栏目:
出版日期:
2019-07-20

文章信息/Info

Title:
Fault Region Extraction of Electrical Equipments in Infrared Images by Pulse-coupled Neural Network Method with Canny Operator

文章编号:
1001-8891(2019)07-0634-06
作者:
冯振新1许晓路1周东国2江翼1丁国成1
1. 国网电力科学研究院 武汉南瑞有限责任公司;
2. 武汉大学 电气与自动化学院
Author(s):
FENG Zhengxin1XU Xiaolu1ZHOU Dongguo2JIANG Yi1DING Guocheng1
1. Wuhan NARI Limited Liability Company of State Grid Electric Power Research Institute;
2. Wuhan University, School of Power and Mechanical Engineering

关键词:
脉冲耦合神经网络电力设备故障红外图像Canny算子
Keywords:
Pulse-coupled neural networkelectronical equipment failureinfrared imageCanny operator
分类号:
TP391
DOI:
10.11846/j.issn.1001_8891.201907007
文献标志码:
A
摘要:
为了较好地实现电力设备红外图像故障区域提取,提出了一种基于Canny算子边界检测的脉冲耦合神经网络(Pulse-coupled Neural Network,PCNN)红外图像区域提取方法。在该方法中,首先以PCNN模型同步点火特性为基础,通过优化原始PCNN模型内在的参数,使得模型迭代过程中将图像转换成为时间点火序列,然后引入Canny边界检测算子并结合区域灰度特性,获取最佳时刻的脉冲输出信息,实现红外图像中热故障区域的有效提取。最后通过真实红外故障图像测试,验证了文中方法的有效性和适用性,同时方便了后续的特征提取与识别。
Abstract:
To implement the extraction of fault regions from infrared images of electronic equipment, in this study, we present a pulse-coupled neural network (PCNN) infrared image region extraction method, which is based on the cooperation of the Canny algorithm. In this method, by using the synchronous pulse characteristics of the original PCNN model, several parameters are simplified to enable the PCNN model to generate time series through iterations. Meanwhile, the canny method is used to improve the ability of the PCNN model to segment infrared images efficiently and extract effective thermal fault regions. Experimental results show that the proposed method has the ability to obtain good segmentation performance and can be suitable for further feature extraction and recognition.

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备注/Memo

备注/Memo:
收稿日期:2019-01-02;修订日期:2019-05-06.
作者简介:冯振新(1985-),男,湖北武汉人,工程师,研究方向为变电设备试验和现场故障检测及诊断。E-mail:13657280380@139.com。
通信作者:周东国(1985-),男,浙江上虞人,讲师,博士,主要研究方向为红外图像处理、模式识别及电力信息处理等方向。E-mail:dgzhou1985@whu.edu.cn。
基金项目:国家电网公司总部科技项目资助(524625160017)。

更新日期/Last Update: 2019-07-12