## 2 HST-Net Model  
In pursuit of enhancing the efficiency of defect detection on the surface of insulators during power equipment inspections in substations, this paper introduces an improved detection model, the HST-Net. As shown in Figure 1, the HST-Net model is composed of three stages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Method** & **Types of Surface Defect** & \begin{tabular}{c} **Computational** \\ **Resource** \\ **Requirements** \\ \end{tabular} & **Robustness** & \begin{tabular}{c} **Inspection** \\ **Speed** \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} Signal Analysis \\ Method \\ \end{tabular} } & Electric Field [4] & \begin{tabular}{c} Missing, fracture, \\ bubbles, debonding, \\ ablation, crack \\ \end{tabular} & Medium & Low & Slow \\ \cline{2-6}  & Microwave [5; 6] & Air-gap, carbonization, \\  & & conductive defect & Medium & Low & Slow \\ \cline{2-6}  & Infrared [8] & Fracture, crack & Medium & Low & Medium \\ \hline \multirow{4}{*}{\begin{tabular}{c} Traditional Image \\ Processing Method \\ \end{tabular} } & DOST-SVM [12] & Broken & Medium & Medium & Medium \\ \cline{2-6}  & \begin{tabular}{c} Similarity \\ matching [14] \\ \end{tabular} & Missing, broken & Low & Low & Fast \\ \hline \multirow{4}{*}{\begin{tabular}{c} Deep Learning \\ Method \\ \end{tabular} } & BN-CBAM [16] & \begin{tabular}{c} Missing, crack, broken, \\ Flashover \\ \end{tabular} & High & High & Medium \\ \cline{2-6}  & Faster RCNN [18] & \begin{tabular}{c} Missing, crack, broken, \\ Flashover \\ \end{tabular} & High & High & Medium \\ \cline{2-6}  & YOLO [19; 21; 22; 23; 24] & \
\begin{tabular}{c} Missing, crack, broken, \\ Flashover \\ \end{tabular} & High & High & Fast \\ \cline{2-6}  & ResNeSt [20] &
\begin{tabular}{c} Missing, crack, broken, \\ Flashover \\ \end{tabular} & High & High & Medium \\ \cline{2-6}  & HST-Net(ours) & Missing, broken, Flashover & Medium & High & Fast \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the status of the proposed model.  
Figure 1: Framework diagram of HST-Net model.  
In the first stage, a Transformer CSP is presented as the backbone network for feature extraction to facilitate the reuse of features and prevent excessive gradient information loss by truncating the gradient flow. The integration of a self-attention module fosters a more effective separation and fusion of prominent and subtle features of surface defects on insulators. In the second stage, a path aggregation network (PANet) is used for the feature refinement. The PANet boosts the semantic flow through a bottom-up pathway and achieves a high-resolution restoration of deep features via a top-down route. This enhances the feature fusion impact, thereby allowing the extraction of richer semantic information from both high-level and low-level image features. The third stage encompasses the decision network and prediction phase. In the head network of the HST-Net, we augment the original object detection head--which includes localization, classification, and confidence--with a segmentation head. This adjustment enables precise pixel-level defect detection, thereby strengthening the semantic understanding of the shape and size of insulator defects.  
The realization of the entire model aims to emulate the actual process of power equipment inspections in substations. Therefore, special attention is paid to handle various interfering environments, ensuring the proposed HST-Net model architecture can be applied to the detection of surface defects in insulators, thus further improving the accuracy and efficiency of defect detection.