字轮式仪表智能图像抄表系统的设计  

Design of image-based intelligent meter reading system for wheel meters

作　　者：顾允迪 徐望明[1,2] 何钦 GU Yun-di;XU Wang-ming;HE Qin(School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China;Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学信息科学与工程学院,湖北武汉430081 [2]武汉科技大学教育部冶金自动化与检测技术工程研究中心,湖北武汉430081

出　　处：《液晶与显示》2023年第7期985-996,共12页Chinese Journal of Liquid Crystals and Displays

基　　金：国家自然科学基金(No.51805386);教育部冶金自动化与检测技术工程研究中心开放课题(No.MADTOF2021B02)。

摘　　要：为了克服人工抄表效率低和现有图像识别方法对双半字符识别不准的问题,设计了基于窄带物联网(NB-IoT)技术和轻量级卷积神经网络(CNN)的字轮式仪表智能图像抄表系统。首先,图像采集终端用NB-IoT模组将摄像头获取的表盘图像上传至云平台;然后,使用局部特征提取与匹配方法估计仿射变换矩阵,将输入的表盘图像转换至模板图像所在坐标空间并分割出各个读数的字符子图像;最后,使用基于多标签分类的轻量级CNN模型识别这些子图像并通过后处理得到最终表盘读数结果。实验结果表明,系统的图像采集终端休眠电流小于10μA,可确保2节锂亚电池工作5年以上;所提出的基于多标签分类的CNN模型能准确识别单字符和双半字符,达到了96.36%的字符识别准确率和94.15%的读数识别准确率,优于对比的其他识别算法。To overcome the problems of low efficiency in manual meter reading and inaccurate recognition for double half-characters by existing image recognition methods,an image-based intelligent meter reading system based on narrow band Internet of things(NB-IoT)and lightweight convolutional neural network(CNN)is designed.Firstly,the image acquisition terminal uses NB-IoT module to upload the meter image collected by the camera to cloud platform.Then,the method of local feature extraction and matching is applied to estimate an affine transform matrix and convert the input meter image to the coordinate space of the template image,and every sub-image of reading character is segment out.Finally,a multi-label classification-based lightweight CNN model is proposed to recognize these sub-images,and the final reading result is obtained by post-processing.Experimental results indicate that the sleep current of the image acquisition terminal of the designed system is less than 10μA,which can ensure two Li/SOCl2 batteries working for more than 5 years,and that the proposed CNN model based on multi-label classification can accurately recognize both single characters and double half-characters and has achieved a character accuracy rate of 96.36%and a reading accuracy rate of 94.15%,which is superior to other recognition algorithms.

关 键 词：窄带物联网 自动抄表系统 卷积神经网络 双半字符识别 多标签分类 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TH86[自动化与计算机技术—计算机科学与技术]