## 1 Introduction  
The research presented in this document focuses on the development and application of a cable-to-terminal connection recognition technology based on pose estimation, specifically utilizing the YOLOv8-pose model. This technology is designed to enhance the efficiency and accuracy of automated inspection systems in substations, which are critical nodes in power transmission and distribution networks. The technology is directly applicable in the routine inspection of substations where it can automate the process of detecting and diagnosing the connection status of cables and terminals. This real-time monitoring capability helps in early fault detection and prevention, thereby ensuring the reliability and safety of the power grid. Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system's diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects. cable-to-terminal connection; YOLOv8-pose; deep learning; pose estimation; object detection  
## 1 Introduction  
The application of 3D connection recognition technology in the modern engineering field is becoming increasingly important [1]. With the continuous advancement of industrial automation and intelligent manufacturing, there is a growing demand for precise detection and monitoring of complex equipment and systems [2]. Traditional 2D image processing techniques can no longer meet the requirements for accuracy and real-time performance in some application scenarios, while 3D connection recognition technology based on pose estimation provides a new solution [3]. With the development of Industry 4.0, industrial electrical automation has become an important part of intelligent manufacturing [4]. Electrical automation systems are widely used in various industrial scenes, such as power plants, substations, and manufacturing industries, achieving the intelligent, digital, and efficient production process through automation technology. In industrial electrical automation, automatic inspection is a key link.