# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 4 Experimental Testing and Analysis
### Identification and Location of Insulators  
In order to verify the reliability of identifying catenary insulators based on the Mask R-CNN algorithm, 545 catenary images were selected as the sample set, of which 484 images were randomly selected as the training set, and the remaining 61 images were used as the test set. Images are annotated with LabelMe.  
Due to the limited number of images collected in the experiment, direct training is prone to over-fitting, which will reduce the accuracy of the actual test. Therefore, in order to avoid the problem of over-fitting in the training process of deep learning, data enhancement methods are adopted before training, including image compression; image blurring; affine transformation (random rotation, translation and scaling); and random adjustment of brightness, saturation, and contrast.  
The GPU of the experimental platform is an RTX 3090, the Pytorch framework is used for model training, and Table 2 shows the setting of hyperparameters in the training process. In addition, ResNet50 is adopted as the feature extraction network, which includes 49 convolutional layers and one fully connected layer. In terms of the complexity of the model, the number of floating-point operations (FLOPs) is about 4 \(\times\) 10\({}^{9}\), and the number of parameters is about 25.5 \(\times\) 10\({}^{6}\), which means that the model has a certain degree of real-time performance.  
Figure 8: The process of catenary insulator damage detection.  
Figure 7: The ten order Zernike moments.  
The curve change of the loss function is shown in Figure 9. After 1000 iterations, the value of the loss function shows a downward trend and the function curve tends to be stable, indicating that the trained Mask R-CNN model converges. After 1000 iterations, the loss function values are shown in Table 3. Using the training results for testing, setting the Intersection over Union (IoU) to 0.5, the mean Average Precision (mAP) of the model reaches 96.4%. The test results of a single image are shown in Figure 10.  
It can be seen from the training and test results that the insulator identification and positioning method based on Mask R-CNN has a high accuracy rate, and the identification effect is stable in different environments, which provides a good condition for the catenary insulator damage detection or diagnosis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Setting** \\ \hline Learning rate & 0.0025 \\ Iterations & 1000 \\ Batch size & 50 \\ Number of epochs & 200 \\ Optimizer & SGD \\ \hline \hline \end{tabular}
\end{table}
Table 2: The setting of hyperparameters.  
Figure 10: Results of insulator identification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Loss Function Name** & \(L\) & \(L_{\text{repn},\text{cls}}\) & \(L_{\text{repn},\text{bbox}}\) & \(L_{\text{cls}}\) & \(L_{\text{bbox}}\) & \(L_{\text{mask}}\) \\ \hline value & 0.2368 & 0.0038 & 0.0140 & 0.0435 & 0.1023 & 0.0733 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Loss function value.  
Figure 9: Loss function curve.