## 2 YOLOv8-Pose Estimation Model
### YOLOv8-Pose Network Structure  
The structure of YOLOv8-pose is divided into four main parts [14], each optimized for specific functions to ensure that the model provides accurate and reliable recognition results under various environmental conditions. The specific network structure is shown in Figure 2.  
The input layer (Input) is responsible for pre-processing the raw image data, including scaling transformation and color adjustment, thereby standardizing the input data and enhancing the model's robustness to different environmental conditions such as lighting changes and occlusions. The pre-processing steps not only improve the model's stability but also reduce the impact of external environments on the detection results. The backbone network (Backbone) is the core module for feature extraction, composed of depthwise separable convolutional layers. Compared with traditional convolutional layers, depthwise separable convolutional layers significantly reduce the computational burden and speed up the inference speed by dividing the convolution operation into depthwise convolution and pointwise convolution. The calculation formulas for depthwise convolution and pointwise convolution are as follows:  
\[\text{Depthwise Convolution}:(\text{I}\times K)_{C}=\sum_{i,j}I_{C(i,j)}K_{( i,j)} \tag{1}\]  
\[\text{Pointwise Convolution}:(\text{I}\times P)_{C}=\sum_{C^{\prime}}I_{C^{ \prime}}P_{(C^{\prime},C)} \tag{2}\]  
where \(I\) is the input feature map, \(K\) is the depthwise convolution kernel, \(P\) is the pointwise convolution kernel, and \(c\) represents the channels. The backbone network extracts multi-level image features by applying convolutional kernels of different sizes, providing rich information for precise object detection and pose estimation. The neck structure (Neck)  
Figure 2: YOLOv8-pose network architecture diagram. connects the backbone network and the detection head, mainly through technologies such as the feature pyramid network and the path aggregation network to fuse features at different levels, enhancing the model's ability to detect objects at multiple scales. The feature pyramid network fuses features at various scales, enabling the model to handle both large and small objects simultaneously, thereby improving the accuracy of object detection and pose estimation. The feature fusion formula of the feature pyramid network is as follows:  
\[P_{i}=Conv(C_{i})+Upsample(P_{i+1}) \tag{3}\]  
In this context, \(C_{i}\) represents the feature map of the \(i\)-th layer, and \(C_{i}\) also denotes the pyramid feature map of the \(i\)-th layer. The Path Aggregation Network further optimizes the transmission and combination of features, ensuring that the model still maintains good detection performance under high-density target environments. The formulas for feature transmission and combination in the Path Aggregation Network are as follows:  
\[P_{i}^{\prime}=Conv\big{(}P_{i}+P_{i-1}^{\prime}\big{)} \tag{4}\]  
In this context, \(P_{i}^{\prime}\) represents the feature map of the \(i\)-th layer after being optimized by the Path Aggregation Network. The detection head (Head) is the output layer of the model, responsible for generating classification results, object bounding box coordinates, and pose information. YOLOv8-pose integrates key-point detection tasks in the detection head part, enabling the model to perform object detection and pose estimation simultaneously. By accurately outputting the spatial positions of the target key points, the detection head can effectively identify the connection points between terminals and cables in the power system, which is crucial for accurately judging the connection relationships. The detection head employs an anchor mechanism and a multi-task loss function, which not only detects the target but also accurately estimates the target's pose, ensuring efficient recognition in various complex scenes.