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 12 M个、60.30 G次、17.04 ms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 0.935 3;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.
Abstract
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 0.935 3,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.
维普
万方数据