## 5 Conclusions  
In this study, we explored the three-dimensional connection recognition method based on pose estimation and conducted a detailed technical analysis and experimental verification by introducing the YOLOv8-pose model. The results of the study indicate that the technology of three-dimensional connection recognition has significant potential and broad prospects in the field of modern engineering.  
Through the optimized design of the YOLOv8-pose model, we successfully achieved an efficient integration of target detection and pose estimation, improving the recognition accuracy and real-time performance in complex backgrounds. Specifically, the model shows excellent performance in the recognition of the connection relationship between terminals and cables, maintaining a high level of accuracy and robustness under various experimental conditions. In addition, by optimizing data pre-processing, annotation strategies, and model training, we significantly enhanced the performance of the model, providing reliable technical support for practical applications in complex industrial scenarios.  
Despite the achievements of this study, there are still some challenges to be addressed. First, the accuracy of pose estimation still needs to be further improved under complex backgrounds and occlusion conditions. Second, the real-time performance and computational efficiency of the model still need to be optimized to meet the needs for efficient, low-resource recognition systems in industrial automation and intelligent manufacturing.  
Future research directions can focus on the following aspects: further optimizing the pose estimation algorithm to enhance the model's recognition accuracy in complex scenes; introducing more efficient deep learning techniques and hardware acceleration to enhance the model's real-time performance; expanding the application range of three-dimensional connection recognition technology to explore its potential application value in other areas of industrial automation.  
In summary, the technology of three-dimensional connection recognition based on pose estimation provides a new solution for the intelligent process in the field of engineering. With continuous technological innovation and optimization, this technology is bound to play an important role in more practical applications in the future, promoting the development of industrial automation and intelligent manufacturing.  
Conceptualization, X.Q.; methodology, X.W.; software, X.W. and G.H.; validation, X.W. and G.H.; resources, Y.L.; data curation, X.Q. and Y.L.; writing--original draft preparation, G.H.; writing--review and editing, Y.L. and X.T.; visualization, X.T. and G.H.; supervision, X.T.; project administration, Y.L.; funding acquisition, X.Q. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Science and Technology Project of State Grid Hunan Electric Power Co., Ltd., grant number 5216A321N00H.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Metrics** & **Result** \\ \hline Precision & 92.8\% \\ Recall & 91.5\% \\ Mean Average Precision & 90.2\% \\ Average Inference Time(s) & 0.074 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection results of the test set after parameter adjustment.