# Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
## 5 Conclusions  
In this paper, the deep learning method and computer vision technology are combined to propose a pointer meter recognition method based on Yolov7 and Hough transform, which realizes the automatic recognition and reading of pointer meters. Deep learning includes Yolov7, DeepLabv3+ and PGNet: use Yolov7 to determine the position of the meter; use DeepLabv3+ to extract the pointer part of the meter; and use PGNet to determine the model and range of the meter. Computer vision includes Thinning and Hough transforms, which can determine the precise position of needles and take meter readings. By harnessing the advantages of deep learning in object detection and image segmentation, it becomes possible to accurately locate the instrument and segment the pointer directly from the image, thereby avoiding the cumbersome process of traditional computer vision for pointer extraction. Therefore, the method effectively solves the problems of uneven illumination, complex background, image blur, and diversity of meter models in each image. The experimental results demonstrate that the utilization of the latest Yolov7 model achieved a remarkable map of 99.8% on the instrument dataset. The accuracy of the pointer readings obtained using this method is more than 95%. The method performs well in terms of anti-interference, accuracy, and robustness. When meter images are captured at highly standard shooting angles, they can be quickly and accurately recognized. However, when meter images are captured at larger angles, the recognition accuracy cannot meet the required standards. In the next phase of the work, efforts will be made to correct the meter images in order to improve the recognition accuracy.  
Writing--review & editing, L.S., D.Z., T.K. and J.L.; Supervision, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Explanation** \\ \hline AR & automatic reading \\ MR & manual reading \\ AT & automatic identification of meter model \\ MT & manual recognition of meter model \\ AM & automatic recognition of the maximum range \\ MM & manual recognition of the maximum range \\ WT & whether the type is the same \\ WM & whether the maximum value is the same \\ AE & absolute error \\ RE & relative error \\ ACC & accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 6: The interpretation of each abbreviation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**AR** & **MR** & **AT** & **MT** & **AM** & **MM** & **WT** & **WM** & **AE** & **RE** & **ACC** \\ \hline
8.22 & 11.90 & A & A & 75 & 75 & Y & Y & 3.68 & 0.05 & 95.09 \\
9.18 & 8.76 & kV & kV & 12 & 12 & Y & Y & 0.42 & 0.04 & 96.50 \\
10.55 & 10.40 & kV & kV & 12 & 12 & Y & Y & 0.15 & 0.01 & 98.75 \\
71.32 & 73.10 & A & A & 150 & 150 & Y & Y & 1.78 & 0.01 & 98.81 \\
21.75 & 23.32 & A & A & 50 & 50 & Y & Y & 1.57 & 0.03 & 96.86 \\
120.78 & 115.25 & V & V & 300 & 300 & Y & Y & 5.53 & 0.02 & 98.16 \\
10.68 & 10.05 & A & A & 15 & 15 & Y & Y & 0.63 & 0.04 & 95.80 \\
161.35 & 155.88 & V & V & 250 & 250 & Y & Y & 5.47 & 0.02 & 97.81 \\
3.47 & 3.05 & A & A & 20 & 20 & Y & Y & 0.42 & 0.02 & 97.90 \\
123.56 & 128.92 & V & V & 150 & 150 & Y & Y & 5.36 & 0.04 & 96.43 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between automatic identification and manual identification of pointer meters.  
Data Availability Statement: [[https://github.com/shilei0406/Pointer-instrument-identification](https://github.com/shilei0406/Pointer-instrument-identification)]([https://github.com/shilei0406/Pointer-instrument-identification](https://github.com/shilei0406/Pointer-instrument-identification)) (accessed on 2 April 2023).