## 3 Experiments
### Experimental Results of Insulator Defect Segmentation  
The results from the training of the HST-Net on the insulator defect segmentation dataset are depicted in Figure 13. The model displays a quick convergence rate, reaching a plateau within the first 200 epochs, at which point the training is halted to prevent overfitting. The maximum mAP score on the insulator defect validation set reached 0.84, with a recall rate of 0.81. Figure 12: Comparison of feature map visualization between C3 and Transformer-CSP. Table 5 presents a comparison among four segmentation models: HST-Net, Unet, OCRnet, and U-Segnet-p. Notably, HST-Net operates with an impressive speed of 83.33 FPS and a smaller model size of 93.245 M. It outpaces Unet, OCRnet, and U-Segnet-p, which run at 51.02 FPS, 4.22 FPS, and 54.81 FPS, and have larger model sizes of 221.857 M, 275.573 M, and 113.986 M, respectively. Moreover, HST-Net achieves a commendable AP of 0.82 and a higher mIoU of 0.89. Additionally, it registers a significantly higher Efficiency score of 1100.0. In contrast, Unet, OCRnet, and U-Segnet-p attain lower AP and mIoU scores, as well as lesser Efficiency scores. It is evident that our HST-Net considerably outperforms the other models, underlining its superior performance in environments such as substations where stringent real-time requirements are necessary. Additionally, the HST-Net is designed with a remarkable consideration for model size. Despite being compact and amenable to edge computing platforms, where processing is often conducted via embedded processors, it maintains a model size within the 100 MB range. This allows for more effortless deployment and better utilization of computational resources, offering a strategic advantage in constrained environments. Besides, the HST-Net does not compromise on precision. With an AP score surpassing 0.82 and a mIoU of 0.89, it demonstrates a superior level of segmentation accuracy. This feature, combined with its compact size and high speed, validates the HST-Net as a highly efficient, precise, and resource-friendly solution for insulator defect segmentation. As illustrated in Figure 14, insulator defect detection segmentation is executed at the pixel level. The inspection algorithm primarily identifies two categories of defects: flashover damage to insulator shells and insulator shell breaks. Flashover damage, the first defect type, is typically the result of excessive voltage stresses or contamination on  
Figure 13: Defect segmentation training results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **FPS** & **Model Size** & **AP** & **mIoU** & **Efficiency** \\ \hline HST-Net (Ours) & 83.33 & 93.245 M & 0.82 & 0.89 & 1100.0 \\ Unet & 51.02 & 221.857 M & 0.65 & 0.57 & 1020.4 \\ OCRnet & 4.22 & 275.573 M & 0.79 & 0.61 & 84.4 \\ U-Segnet-p & 54.81 & 113.986 M & 0.76 & 0.60 & 647.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of four segmentation models. the insulator surface. It presents itself as charred or discolored patches on the insulator shell, indicating areas where electricity has bypassed the intended conductive path and traversed through the insulator instead. Prolonged exposure to such conditions may further weaken the insulator's dielectric properties and lead to catastrophic system failures. The second defect type, breaks in the insulator shell, usually occur due to mechanical stresses or environmental factors such as wind, ice, and even wildlife interference. Such defects might compromise the insulator's mechanical integrity and lead to catastrophic equipment failure if not detected and addressed promptly. From the segmentation results shown in Figure 15, it is evident that the Unet algorithm provides the least satisfactory performance. Two large areas of insulator defects are overlooked by this algorithm, resulting in a substantial gap in defect identification. The OCRnet model, on the other hand, produces a false positive, mistakenly identifying a normal white area as a \"Broken\" region.