# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 1 Introduction  
The advancement of Industry 4.0 and intelligent manufacturing has heightened the significance of industrial instrumentation in intelligent transformation applications [1]. In modern industrial facilities, analog pointer instruments extensively monitor critical parameters such as pressure, temperature and flow rates, serving as the primary safeguard for operational safety. Pointer meters, owing to their uncomplicated structure, robust durability, and cost-effective maintenance, are widely utilized across industrial sectors including petroleum and electric power [2]. However, the conventional reading method predominantly depends on manual periodic collection due to the inability of the pointer meter to directly acquire readings via the communication interface. This approach is inefficient and lacks real-time performance assurance. Moreover, it is susceptible to subjective operatorinfluence, leading to data inaccuracies. These limitations are particularly pronounced in extreme environments characterized by high temperatures, pressures, and radiation levels [3]. In extreme environments, such as those characterized by high temperatures, pressures, and radiation levels, the limitations of manual data collection methods become particularly evident [4]. In industrial settings with restricted human access, such as nuclear power plants and oil refineries, there is a growing demand for automated solutions to address safety concerns. The automation of meter reading has progressed through various technological advancements, from early electromechanical systems to the current vision-based approaches. In recent years, the adoption of inspection robots has represented a significant step forward in industrial automation [5]. Wheeled inspection robots are commonly employed in industrial settings due to flexibility, mobility, and cost-effectiveness. However, the effectiveness of these robots is often hindered by the inherent limitations of their camera systems, including fixed focal lengths and limited zoom capabilities. These constraints can lead to issues such as image blurring, distortion, and scale variations [6]. Consequently, there is a pressing need to develop more precise methods for accurate meter reading recognition to address these challenges effectively. Currently, automatic recognition of pointer instruments can be broadly categorized into two approaches: traditional computer vision-based methods and deep learning-based methods. Traditional image processing methods have been commonly employed for meter reading recognition due to their straightforward implementation and computational efficiency. [PERSON] et al. pioneered the application of image processing and computer vision techniques for the automatic reading of pointer meters [7]. Similarly, [PERSON] et al. introduced a distance discrimination method utilizing machine vision to precisely position graduation marks and pointers [8]. Furthermore, [PERSON] et al. proposed the concentric ring search method, which demonstrates improved accuracy in reading pointer meters deflection angles [9]. However, traditional image processing methods exhibit limitations in adapting to changes in illumination, occlusion, and other interferences, and they often struggle with weak generalization capabilities and are susceptible to variations in instrument appearance, background noise, and other factors, leading to suboptimal detection outcomes in complex environments. The emergence of deep learning has significantly transformed the field of meter reading, primarily due to its advanced feature learning capabilities in complex environments [10; 11; 12]. Presently, deep learning-based instrument recognition models are generally divided into two categories: two-stage detection models, such as Faster R-CNN, and single-stage detection models, like YOLO. [PERSON] et al. [13] and [PERSON] et al. [14] employed Faster R-CNN to identify pointer dial regions, while [PERSON] et al.