## 1 Introduction
[PERSON] et al., have put forward an innovative intelligent end-to-end method for reading pointer meters utilizing deep learning techniques. This approach identifies the meter and captures the pointer concurrently without the need for pre-existing information. Tests demonstrate that this method outperforms several standard methods in both speed and efficacy [19]. [PERSON] et al., have developed a Lite-FCOS guided by scale value for recognizing readings from pointer meters, employing Hough transform and analysis of pointer distribution to ascertain the direction of the meter pointer [20]. Within the realm of these algorithms, R-CNN stands as a foundational approach to region-based object detection, a process which partitions the task of detection into discrete phases: initially, the algorithm identifies areas within an image that potentially contain objects--a step known as target localization. Subsequently, it classifies each localized region into specific categories depending on the objects they are believed to contain. The R-CNN and its subsequent iterations, such as Fast R-CNN and Faster R-CNN, leverage this two-stage strategy to fine-tune the precision of object detection. They are particularly adept at discerning intricate details and managing the presence of small or overlapping objects against complex backgrounds. However, the increased accuracy offered by these algorithms comes at the price of computational efficiency; the need to generate and individually analyze multiple region proposals for each image makes them less suitable for applications requiring swift decision-making in real-time scenarios. YOLO (You Only Look Once) exemplifies the single-stage object detection paradigm, eschewing the multi-stage process in favor of a unified, holistic approach. YOLO transforms the task of object detection into a solitary regression challenge, concurrently determining the bounding boxes and class probabilities in one go directly from entire images. This streamlined method significantly accelerates the detection process, offering a considerable advantage in scenarios where real-time processing is crucial. To minimize environmental interference on measurement accuracy and boost the precision and robustness of arrester instrument scale recognition, this study introduces a deep learning-based recognition method. The schematic diagram of the research methodology for this study is shown in Figure 1.  
In this paper, the Methods section delves into the technical aspects of the proposed solution, detailing the integration of attention mechanisms into the YOLOv5 model and the subsequent steps for instrument scale recognition. In the Results section, the outcomes of the experimental evaluation of the proposed method on a specialized dataset will be presented. Metrics such as precision, recall, and mean average precision are used to quantify the model's performance. The Discussion section interprets the significance of the study's findings, noting the high success rate in recognition and the positive impact of attention mechanisms on model accuracy. The Conclusion section encapsulates the main achievements of the research, emphasizing the practical implications for substation monitoring automation and suggesting avenues for future development in intelligent industrial systems.