基于R-YOLOv7和MIMO-CTFNet的指针式仪表自动读数方法  

Automatic reading of pointer meters based on R-YOLOv7 and MIMO-CTFNet

作　　者：李盛涛 侯立群[1] 董亚松 LI Shengtao;HOU Liqun;DONG Yasong(Department of Automation,North China Electric Power University,Baoding Hebei 071003,China)

机构地区：[1]华北电力大学自动化系,河北保定071003

出　　处：《图学学报》2024年第6期1313-1327,共15页Journal of Graphics

基　　金：河北省自然科学基金(F2016502104)。

摘　　要：针对现有方法中表盘关键信息提取过程繁琐、读数误差较大和相机抖动导致的运动模糊问题,提出了一种基于R-YOLOv7和MIMO-CTFNet的指针式仪表自动读数方法。首先,构建兼顾精度和轻量化的R-YOLOv7算法实现指针式仪表表盘和表盘关键信息检测;然后,设计了MIMO-CTFNet算法以实现运动模糊仪表图像的复原;最后,利用提取的表盘关键信息进行基于小刻度线的角度法读数。实验结果表明改进后的R-YOLOv7在表盘关键信息检测数据集上所需的参数量、FLOPs、ADT和mAP50:95分别为12M个、60.30G次、17.04ms和86.5%;改进后的MIMO-CTFNet算法在采集的运动模糊数据集上的PSNR和SSIM分别达到33.05 dB和0.9353;该读数方法的读数最大引用误差为0.35%,需要运动模糊处理和无需运动模糊处理的图像读数时间分别为0.561 s和0.128 s,从而验证了该方法的有效性。To solve the problems in current pointer meter reading methods,such as the complicated reading process,significant reading errors,and the motion blur caused by camera shakes,an automatic reading method based on R-YOLOv7 and MIMO-CTFNet(multi-input multi-output CNN-transformer fusion network)was proposed.First,the R-YOLOv7 algorithm was constructed to consider both accuracy and lightweight for detecting the dial and its key information.Then,a MIMO-CTFNet algorithm was designed to recover the motion-blurred meter images.Finally,the angle method based on the extracted small scales was utilized to perform meter reading.The experimental results showed that for the data set of dial key information finding,the parameters,FLOPs,ADT,and mAP50:95 were 12 M,60.30 G,17.04 ms,and 86.5%,respectively.The PSNR and SSIM of the improved MIMO-CTFNet algorithm achieved 33.05 dB and 0.9353,respectively.The maximum fiducial error of the proposed reading method was 0.35%,and the reading time for images requiring and not requiring motion blur was 0.561 s and 0.128 s,respectively,validating the effectiveness of the proposed method.

关 键 词：指针式仪表 R-YOLOv7 MIMO-CTFNet 自动读数 轻量化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TH70[自动化与计算机技术—计算机科学与技术]