## 1 Introduction
The traditional recognition techniques previously mentioned are heavily reliant on the use of either isolated or manually configured features to accurately pinpoint the scales present on various instruments. However, these methods tend to fall short when subjected to environments characterized by their complexity and a high degree of variability, where their performance in terms of accuracy and robustness is often compromised. In the realm of pointer detection, a favored approach is the application of the Hough transform algorithm. Despite its widespread use, this algorithm is notably susceptible to the detrimental effects of image noise, resulting in a performance that is less than ideal in terms of its anti-interference properties. With the advancement of deep learning, numerous detection algorithms stemming from this field have been proposed, including the YOLO series [9; 10; 11; 12; 13] and the R-CNN series [14; 15; 16; 17; 18]. [PERSON] et al., have developed the YOLOX-CAlite algorithm for meter detection to address the specific performance challenges encountered in target detection within the context of meter reading. The tests conducted demonstrated that this refined YOLOX-CAlite version reached an average precision (\(AP\)) rate of 90.4% [9]. [PERSON] et al., have suggested a technique that combines Yolov7 with the Hough transform for the recognition of pointer meters, aiming to improve their automatic readability. The model's mean Average Precision (\(mAP\)) on the instrument dataset using Yolov7 has achieved an impressive high of 99.8%. Moreover, the accuracy of the readings from pointers using this technique surpasses 95%, indicating its potential for extensive application in numerous situations [10]. [PERSON] et al., have engineered a smart recognition technology for pointer instruments that leverages YOLOv5, enabling swift location and reading identification of these instruments [11]. [PERSON] et al., have presented a smart vision recognition approach that integrates YOLOv5 with the U-2-Net network (YLU2-Net) to boost the precision and speed of meter identification in intricate settings. The trials conducted validate the effectiveness and performance of the YLU2-Net method they propose [12]. [PERSON] et al., have developed a technique that utilizes YOLOv3 for the categorization and reading detection of industrial instruments, capable of interpreting both pointer-type and digital meters [13]. [PERSON] et al., have introduced a flexible detection and identification system for pointer meters using computer vision, adaptable to a range of settings. The system employs a Faster Region-based Convolutional Network (Faster R-CNN) to accurately identify the location of the meter of interest, subsequently adjusting the camera according to the detection box. Test outcomes show the system's reliability and precision, verifying its effectiveness across diverse scenarios [14]. [PERSON] et al., have put forward a recognition model that utilizes convolutional neural networks along with a unique virtual sample creation technology. This technology can produce a multitude of images from a small number of actual instrument images, effectively facilitating the training of the recognition model [15]. [PERSON] et al., have presented a novel and reliable method for recognizing readings on pointer meters, achieving autonomous recognition [16]. [PERSON] et al., have introduced an innovative method for recognizing pointer meters that combines lightweight convolutional neural networks (CNN) with wireless sensor networks (WSNs). This approach encompasses image pre-processing, CNN-based classification, and calculation of readings at the WSN endpoint, which notably decreases data transmission by sending only the recognized outcome across the WSN [17]. [PERSON] et al., have explored an automatic method for reading industrial pointer meters to tackle present challenges, by incorporating object detection through the Faster Region-based Convolutional Network (Faster-RCNN) combined with classic computer vision techniques [18].