摘要
为了解决电力系统海量非结构化图像数据智能化分析和识别这一问题,提出了一种结合深度学习和随机森林的电力系统关键电力设备图像识别方法。在特征提取方面,通过卷积神经网络提取了电力设备图像的特征;在识别算法方面,借鉴传统机器学习方法的优势,提出了结合深度学习的随机森林分类方法。使用8 500幅电力设备图像对该方法进行了测试。研究结果表明:对于绝缘子、变压器、断路器、输电线电杆和输电线铁塔这5种电力设备,该方法的平均识别准确率达到了89.6%,比常规卷积神经网络分类器和传统随机森林分类器的平均识别准确率分别高出了6.8%和12.6%。该方法为海量非结构化电力设备图像智能化分析提供了一种新的解决办法。
In order to analyze and recognize mass unstructured multimedia data in the electric power department auto- matically, we propose a new method which applies random forest to cIassify the electric equipment images by using features extracted by deep convolutional neural network. To be more specific, the CNN-based AlexNet model is used to extract features from the electric equipment images firstly. Then, benefitting from the achievements of digital image pro- cessing technology, pattern recognition technology, and machine learning technology, random forest is applied to classify the electrical equipment into different categories based on the deep learning features. Moreover, in order to reduce the feature redundancy, a fisher＇s criterion based method is proposed to select features, which are much more effective for random forest classifier than the traditional feature selection method. An electric equipment image database which con- tains eight thousand and five hundred electric equipment images is constructed to test the efficiency of the proposed method. There are five types of electric equipment： insulators, power transformers, breakers, power poles, and power towers in the database. Research indicates that the recognition accuracy of the proposed method is 89.6%, which is 6.8% higher than that of the softmax-based deep convolutional neural network and 12.6% higher than that of the traditional random forest. Furthermore, it can effectively eliminate the effects which are produced by the complex background. In conclusion, the proposed method can meet the actual demands of the electric power department, and it provides a new solution for intelligent analysis and recognition of unstructured mass electric equipment images.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2017年第11期3705-3711,共7页
High Voltage Engineering
基金
广东电网公司重点科技项目(GDKJQQ20152015)
湖北省科技厅自然科学基金(2016CFB460)~~
关键词
电力设备
图像识别
智能分析
深度学习
随机森林
卷积神经网络
electric equipment
image recognition
intelligent analysis
deep learning
random forest
convolutional neural network
作者简介
李军锋（通信作者）1979-，男，博士生，高工.主要从事控制科学与工程、计算机仿真方面的研究工作.E-mail：henanjunfeng@163．com;王钦若1958-，男，硕士，教授，博导.主要从事自动化装备技术、智能控制等方面研究工作。E-mail：Wangqr2006@gdut．edu．cn;李敏1978-，女，博士，副教授主要从事图像处理与模式识别等方面的研究工作.E-mail：reaphope@163.com
