## 5 Conclusions  
In this study, we have innovatively incorporated a CA (Channel Attention) attention mechanism module into the YOLOv5 object detection model framework. This module significantly enhances the model's proficiency in extracting relevant features, which is particularly crucial for the accurate recognition and localization of arrester pointer-type instruments. Leveraging the OpenCV library, we carried out meticulous dial correction, which significantly refined the accuracy of scale recognition.  
Despite the introduction of the attention mechanism module contributing to an increase in the model's complexity--and consequently, a slight uptick in the average detection time for the modified CBAM-YOLOv5, CA-YOLOv5, and SE-YOLOv5 models in comparison to the baseline YOLOv5 model--the trade-off has proven to be beneficial. The improvements in mean average precision (\(mAP\)) are noteworthy, with increases of 1.4%, 4%, and 2.8% for the aforementioned models, respectively. These metrics not only validate the efficacy of the attention mechanism in bolstering the extraction of essential features but also showcase the potential for more sophisticated object detection capabilities in complex real-world scenarios.  
Our rigorous testing on a specialized dataset of arrester pointer-type instruments yielded impressive results. The proposed method for scale recognition attained a remarkably high success rate of 96% within the defined acceptable error margin. Moreover, the method maintained an impressively low average error rate of 0.923% while operating at commendable speeds, averaging a mere 0.662 s for image processing. This performance surpasses that of conventional methods, such as the SURF (Speeded Up Robust Features) recognition algorithm and the U-net model, by a significant margin. The notable improvements in accuracy and speed clearly demonstrate the superiority of our method, confirming its potential for widespread adoption in practical applications where rapid and precise scale recognition is paramount.  
In conclusion, the study successfully addresses the challenges posed by the recognition of arrester pointer instruments in various scenarios. The CA-YOLOv5 model's impressive accuracy and efficiency in recognizing dial scales against diverse backgrounds and distances demonstrate the power of deep learning in complex pattern recognition tasks. The thorough validation of the proposed method against a specialized dataset confirms its viability for practical application and sets a new benchmark for automated scale recognition technology.  
This study represents a substantial contribution to the automation of substation monitoring and opens up new avenues for research and development in intelligent industrial systems.  
Conceptualization, H.W. and H.Y.; methodology, H.Y.; software, Y.H.; investigation, Y.H.; resources, H.W.; data curation, Y.H.; writing--original draft preparation, H.W.; writing--review and editing, Y.C.; visualization, Y.C.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China (grant number 12172255), Tianjin Science and Technology Plan Project (grant number 23 YDTJPJC00390) and Tianjin Municipal Education Commission Scientific Research Plan Project (grant number 2022 ZD002).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.