一种基于Faster RCNN的电网图元识别方法  

Detecting Electrical Circuit Elements Based on Faster RCNN

作　　者：徐剑 张皓 徐航[1] 解凯[2] XU Jian;ZHANG Hao;XU Hang;XIE Kai(Beijing Electric Power Corporation,SGCC,Beijing 100031,China;NR Electric Co.Ltd.,Nanjing 211102,China)

机构地区：[1]国网北京市电力公司,北京100031 [2]南京南瑞继保电气有限公司,江苏南京211102

出　　处：《计算机与现代化》2021年第12期53-57,共5页Computer and Modernization

摘　　要：电网工程图纸矢量化识别是实现电网领域基础图纸数字化的一种重要技术途径。由于电网中的电气元件类别多,其中部分图像背景模糊,电气元件的旋转角度不一致,从而对图纸中的电气元件的识别造成一定挑战。本文提出一种基于深度学习中Faster RCNN网络架构的电气元件识别和训练方法,将需要训练的样本数据进行预处理和特征提取,在预处理过程进行平滑去噪、二值化、分割等操作,在特征提取过程采用VGG16网络,利用深度学习方法对电网图元进行识别。在包含9类电网图元的真实数据集上的实验结果表明,本文方法对电网工程图纸中的电气元件的识别和检测具有较好的效果。Vectorization of engineering drawings plays a key role in the digital foundation of power grid.Due to the variety of electrical components in power grid,some image backgrounds are blurred,and the rotation angle of the electrical components is not consistent,which poses a challenge for the identification of the electrical elements in the drawings.This paper mainly studies the electrical element recognition and training with the Faster RCNN network architecture in deep learning,and performs preprocessing and feature extraction on the images to be trained,including preprocessing in smooth denoising,binarization,segmentation,etc.The feature extraction uses VGG16 network,and then uses Faster RCNN for classification.Experimental results on real-world datasets with 9 categories of electrical circuit elements show that the performance of detection and classification of electrical elements are efficient.

关 键 词：电网图元 Faster RCNN 目标检测 

分 类 号：TP393.03[自动化与计算机技术—计算机应用技术]