# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 1 Introduction
[15] enhanced Mask-RCNN for low-light conditions, improving recognition accuracy through image tilt correction. However, these models often suffer from complex parameters and slow inference speeds, which limit their effectiveness in real-time industrial monitoring. Conversely, single-stage detection models are better suited for industrial applications due to their enhanced real-time performance and ease of deployment. Advances in the YOLO series have improved the feature extraction capabilities of pointer meters through network optimization and attention mechanisms. [PERSON] et al. developed a simplified approach for instrument pointer recognition using an improved version of the YOLOv5-MRL algorithm [16]. [PERSON] et al. 's YOLOv5-MR model achieves reading recognition by simultaneously detecting eight target types, such as the dial center, pointers, and six scale points, while implementing ellipse fitting. However, its multi-target joint detection mechanism is susceptible to error accumulation in occluded or blurred scenarios [17]. Despite these advancements, existing methods still face challenges such as noise interference sensitivity, significant tilt shooting errors, and the diversity of instrument types. Furthermore, complexities such as diverse illumination backgrounds and shooting distortions in real-world settings further complicate meter reading recognition. In industrial settings, leveraging prior information from the fixed installation of instruments (e.g., category and range information obtained from configuration files by inspection robots) can enhance algorithm stability. This paper introduces a novel approach for pointer meter reading recognition using YOLOv11-OBB, which integrates deep learning and traditional image processing techniques. The primary objective is to enhance the accuracy of recognizing pointer meter readings and improve the method's generalizability. This paper can be summarized as follows. 1. Rotated Object Detection Network Designing: This paper presents YOLOv11-OBB, a rotated object detection network built upon the YOLOv11 framework. By employing rotation matrix-based bounding boxes, the network achieves precise localization of pointer positions and orientations with high accuracy. A long-edge representation scheme combined with circular smooth labeling (CSL) is introduced to robustly discriminate pointer directions while enabling detection of dial centers in a unified learning framework. 2. Adaptive Model Library Construction and Matching: A transformation relationship between inspection and template images is established through a pointer table model library. Key variables are identified and stored following standard model registration and calibration fitting. The transformation matrix is computed using feature point matching algorithms, brute force matching, and the RANSAC algorithm to ensure precise alignment between inspection and template images. This method improves the system adaptability across different instruments and facilitates instrument detection in complex industrial environments. 3. Experimental Verification and Performance Analysis: The proposed method demonstrated exceptional performance in handling pointer instrument datasets, achieving a mean Average Precision (mAP) of 99.5%, an average relative error of 0.4157%, and a maximum relative error of 1.1464%. Furthermore, the method's robustness was validated by its ability to accurately process low-quality images with various impairments, including blur, darkness, overexposure, and tilted images, while still meeting industrial standards. These findings establish the method as a highly adaptable and reliable automated reading solution for pointer instruments in the intelligent industrial field, underscoring its substantial practical engineering value. This study presents a novel pointer reading recognition method that integrates the YOLOv11-OBB rotated object detection network with template matching technology.