摘要
基于可见光图像的绝缘子识别是电网智能巡检设备识别的最重要的任务之一。电网智能巡检采集的图片存在着有效样本稀缺及场景多样性等问题,极大地限制了深度学习等技术在电网设备智能识别上的应用。本文基于深度学习对轻量级的多来源图像样本数据进行了绝缘子识别的探索和应用。首先,阐述了基于深度学习的目标识别算法发展过程,并着重介绍和对比了区域卷积神经网络(region-convolutional neural network,R-CNN)、快速区域卷积神经网络(fast region-convolutional neural network,Fast R-CNN)和更快区域卷积神经网络(faster region-convolutional neural netw ork,Faster R-CNN) 3种用于目标识别的深度学习模型。然后,通过对来源于不同场景的百量级绝缘子图像进行实验,验证了Faster R-CNN模型在百量级图像中和可用性和鲁棒性。本文的研究和实验为深度学习技术在电网各类设备图像目标识别上的推广和应用探索了一条有效路径。
Visible light image based insulator recognition is one of the most important tasks of intelligent inspection equipmet identification in power grid.Generally,the samples captured from intelligent inspection in power grid are of low availability and contain multi-scenes.These problems greatly limit the application of deep learning in intelligent recognition of power grid equipment.In this paper,a small number of multi-source images are explored and applied in insulator recognition based on deep learning.Firstly,the development process of object recognition algorithm based on deep learning is described.Secondly,three deep learning models for object recognition are emphatically introduced and compared,which are region-convolutional neural network(R-CNN),Fast region-convolutional neural network(Fast R-CNN)and Faster region-convolutional neural network(Faster R-CNN).Finally,through experiments on a hundred magnitude insulator images from different scenes,the usability and robustness of the Faster R-CNN model are verified.The research and experiment in this paper have explored an effective way for the application of deep learning technology in the object recognition of various equipment in power grid.
作者
黄文琦
张福铮
李鹏
明哲
许爱东
陈华军
杨航
HUANG Wenqi;ZHANG Fuzheng;LI Peng;MING Zhe;XU Aidong;CHEN Huajun;YANG Hang(Electric Power Research Institute,CSG,Guangzhou 510663,China)
出处
《南方电网技术》
北大核心
2018年第9期22-27,共6页
Southern Power System Technology
基金
南方电网公司科技项目(ZBKJXM20170086)~~
