# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 5 Conclusions  
This paper presents an intelligent approach for recognizing pointer meters in complex scenes to tackle challenges such as reduced detection accuracy and lack of robustness in current reading recognition methods based on object detection. The proposed method combines rotating object detection with adaptive template matching. Initially, the YOLOv11 object detection algorithm is employed to improve the feature extraction capacity or pointer rotation direction and dial center by incorporating a rotation bounding box (OBB) detection mechanism. Subsequently, a diverse library of pointer instrument templates is established, and the spatial transformation relationship between the inspection image and registration template is defined. Finally, an angle analysis algorithm is employed to establish a mathematical mapping model for pointer deflection angle and instrument range, enabling accurate reading calculations.  
The experimental results reveal that the pointer reading recognition method, integrating YOLOv11-OBB with classic template matching, achieves a mean average precision (mAP) of 99.5% on a custom pointer instrument dataset. The method registers an average relative error of 0.4157% and a maximum relative error of 1.1464%. It exhibits exceptional adaptability and robustness in complex industrial environments, offering a reliable solution for automatic pointer instrument readings in intelligent industrial contexts. This approach has been validated in engineering practice and is ready for direct application in industrial inspections.  
Figure 12: Recognition results of low-quality meter images.  
This approach faces several limitations. Firstly, the conventional SIFT feature point matching algorithm used in template image matching often produces outliers when comparing real-time inspection images with templates, especially under image blurring or distortion, complicating outlier elimination. Future research will enhance outlier elimination by integrating the GMS (Grid-based Motion Statistics) algorithm and expanding key point matching research based on the SIFT algorithm. Secondly, the detection accuracy of the current YOLOv11-OBB model, while satisfactory on standard datasets, still requires refinement for blurred images and distant targets. Future research endeavors will leverage recent advancements in lightweight attention mechanisms and hybrid architectures to explore the integration of optimized attention modules into the YOLOv11-OBB framework, with the aim of enhancing its capability to detect small, distant targets under challenging imaging conditions. Lastly, the current template library approach requires manual registration of templates for each new instrument type, resulting in a linear increase in maintenance costs as the number of instrument types grows. Future research will explore the use of transfer learning to automate template generation, with the aim of enhancing the system's detection capabilities for previously unknown instrument types.  
Methodology, L.W.; Software, C.D.; Investigation, B.H.; Writing--review and editing, X.X. All authors have read and agreed to the published version of the manuscript.  
Naval Engineering University Independent Research Project (No. 2023504040) [October 2023-October 2025]. National Key Research and Development Program of China (No. 2022 YFC3102800) [November 2022-November 2025].  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.