# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 4 Experimental Results
### Pointer Meter Reading Recognition Experiment
#### 4.3.2 Experimental Results and Analysis  
(1) Experimental Results and Analysis under Normal Condition  
To evaluate the proposed pointer recognition method, we conducted experiments on pointer meter reading recognition, detailed in Table 3. The results indicate that the proposed recognition method effectively accomplishes the pointer recognition task, achieving an  
Figure 10: Training metrics comparison curve. (**a**) Comparison of model precision; (**b**) Comparison of model recall; (**c**) Comparison of mAP@75; (**d**) Comparison of mAP@50-95.  
average relative error of 0.41568% and a maximum relative error of 1.1464%. Partial experimental results are illustrated in Figure 11. The method by [PERSON] et al. (2015) [29] utilized threshold segmentation via machine vision coupled with the Hough transform, resulting in a mean relative error of 0.613%. Alternatively, [PERSON] et al. (2018) [30] implemented pointer detection through dynamic feature analysis, yielding a mean relative error of 1.375%. In comparison, our proposed method achieves a mean relative error of 0.41568%, demonstrating superior performance over these traditional approaches. (2) Experimental results and analysis of low-quality meter images  
To assess the proposed method's effectiveness in processing low-quality meter images, this study compares recognition results for four types of low-quality meter images, as shown in Figure 12.  
The recognition experiment conducted on low-quality meter images demonstrates the effectiveness of the method presented in this study in detecting pointer and dial center positions with readings falling within acceptable error margins. Notably, the proposed method exhibits the capability to accurately identify readings in low-quality meter images. Table 4 provides a detailed summary of the identification results for such images, revealing maximum relative errors of \(-0.797\%\) for blurred images, 0.488% for darkness images, 0.507% for overexposure images, and 0.538% for tilted images. These errors comply with the criteria outlined in the China Electric Power Industry Standard Inspection Robot Inspection Technical Specification for pointer meter algorithms, stipulating an error margin of less than \(\pm 5\%\). The recognition errors for all low-quality images remain within acceptable limits. While the method's performance may slightly diminish when handling blurred images, it maintains high accuracy with most standard meter images and demonstrates exceptional robustness in reading recognition under adverse conditions. Furthermore, a comparative analysis with reference [11] indicates our method's superior control over maximum relative errors in low-quality image recognition, achieving \(-0.797\%\) for blurred images and 0.538% for tilted images, compared to reference [11]'s 0.27% and 1.33%, respectively. Notably,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No. ** & \(\mathbf{v}\) & \(\mathbf{v}^{\prime}\) & \(\mathbf{L}\) & \(\mathbf{\varepsilon}\) & **No. ** & \(\mathbf{v}\) & \(\mathbf{v}^{\prime}\) & \(\mathbf{L}\) & \(\mathbf{\varepsilon}\) \\ \hline
1 & 0.815 & 0.81675 & 1.6 & 0.1094\% & 23 & 9.45 & 9.49973 & 1  
the error reduction for tilted images is about 60%. Additionally, our method keeps the absolute error within 0.797% for low-quality images, surpassing the maximum error of 1.33% reported in reference [11].