# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 2 Previous Works  
### Pointer Detection  
The precision of automated pointer meter reading relies significantly on the accurate identification of pointer and graduations [18]. Consequently, it is critical to precisely extract the meter hands, scales, and graduation values from the dial image. Conventional methods for pointer detection include threshold segmentation, edge detection, and straight-line detection [19]. Threshold segmentation is a technique that segments the image based on pixel gray or color values. This method directly distinguishes between the pointer and background by selecting an appropriate threshold (global, adaptive, or the Otsu algorithm). It is characterized by its low computational complexity and high real-time performance, particularly effective when there is high contrast between the pointer and background [20]. However, in cases of uneven illumination or complex backgrounds, a single threshold may be insufficient for accurate segmentation, leading to sensitivity to noise [21]. In contrast, the edge detection method aims to eliminate interference from uniform areas by emphasizing the outline edges of the pointer. Nonetheless, it is susceptible to disturbances from non-pointer edges, such as scales, text, or decorative textures. On the other hand, line detection involves identifying and parameterizing line endings in an image, commonly achieved through the Hough transform. A challenge with this method is the potential misjudgment of non-pointer lines, like dial scales and frames [22]. Moreover, variations in illumination or external occlusions can render preset parameters ineffective, thereby reducing the overall efficacy of these methods. [PERSON] et al. utilized U-Net for the extraction of graduation marks and pointer from pointer instruments to achieve automated reading, leveraging U-Net segmentation outcomes and contour fitting of graduation marks [23]. However, the U-Net network is susceptible to losing boundary details of small-scale objects during image segmentation. Furthermore, the convolution kernel characteristics of convolutional neural networks limit the ability to capture global and long-dimensional features from pointer meter images, leading to incomplete segmentation of lengthy pointers. To solve this problem, [PERSON] et al. addressed this issue by utilizing the Swin Transformer to partition the network for producing mask images of scales and pointers, subsequently utilizing the minimum skeleton circle method to adjust the pointer line equation [24]. This approach, while enhancing feature extraction, is impractical for real-world applications due to its time-consuming nature. [PERSON] et al. used the YOLOv5-MRL multi-scale detection model to accurately identify components like the instrument pointer and dial center, improving enhancing small pointer detection precision [16]. To address the challenge of pointer direction discrimination, [PERSON] et al. introduced a novel rotating object detection network based on YOLOv5, inspired by remote sensing object detection algorithms [25]. Following the analysis presented above, the YOLOv11-OBB rotational object detection network has been incorporated into the YOLOv11 framework. This integration enables precise localization of the pointer's position and angle through the utilization of rotating matrix boxes. The network also distinguishes the pointer's direction by utilizing the long edge representation of circular smooth labels enabling accurate identification of both the pointer's position and direction in complex intricate environments.