# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 2 Insulator Identification and Location Based on Mask R-CNN
### Network Structure  
The network structure of Mask R-CNN algorithm mainly includes four parts: the feature pyramid network (FPN), region proposal network (RPN), ROI Align (Region of Interest Align), and target detection and instance segmentation [21; 22; 23]. The specific network structure for catenary insulator identification is shown in Figure 1.  
The main function of the Feature Pyramid Network (FPN) is to distinguish targets with different sizes in the image and different features of targets [19]. As shown in Figure 2, the pyramid network structure is used to convolute and pool the image, extract feature maps with different sizes, and then use \(1\times 1\) Size convolution kernel to reduce the dimension \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Implementation Method** & **Characteristic** \\ \hline SSD & Multiple layers are used for object detection, lower layers have smaller scales and higher layers have larger scales; Fully convolutional layers are used to predict confidence scores and localization offset. & Poor performance on small objects due to lower layers lacking deep semantic information. \\ \hline YOLO & Input image is divided into \(k\times k\) grids and object detection is performed on each grid. & Low localization accuracy due to poor discrimination when object is partially located in one grid. \\ \hline Faster R-CNN & A regional proposal network (RPN) is adopted in the first stage, and the proposals are further classified and localized through the ROI (Region of Interest) Pooling layer in the second stage. & The ROI Pooling layer adopts a quantitative rounding method, which will produce characteristic dislocation. \\ \hline Mask R-CNN & FPN (Feature Pyramid Network) is used to extract features and RPN (Regional Proposal Network) is used to generate proposals in the first stage. & The ROI Align adopts a bilinear interpolation method, which can avoid feature dislocation; \\  & ROI Align is used for location and classification, and mask branch is used for instance segmentation in the second stage. & the mask branch is added, and the accuracy of small target detection can reach pixel level. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different target recognition algorithms. Figure 1: Network structure of catenary insulator identification based on Mask R-CNN. of the features, so that the number of feature maps in each stage is the same. Then, the up-sampling operation is used to make the size of the feature maps the same, so as to realize the fusion of different size feature maps in each stage. The original Mask R-CNN algorithm used ResNet101 as the backbone of the feature pyramid network, which has about 44.5 \(\times\) 10\({}^{6}\) parameters and 8 \(\times\) 10\({}^{9}\) FLOPs, while the relatively simple ResNet50 has only about 25.5 \(\times\) 10\({}^{6}\) parameters and 4 \(\times\) 10\({}^{9}\) FLOPs. Considering the real-time and robustness of the algorithm, ResNet101 is replaced by ResNet50 in this paper [24]. The main role of the Region Proposal Network (RPN) is to calculate the candidate box to represent the position of the object in the image. The feature map output by the FPN layer is used as the input of the RPN layer. First, a 3 \(\times\) 3 shared convolution operation is performed for each feature map, and then two different sliding windows are processed, one of which uses softmax to distinguish the foreground and background, and the other is used to calculate the offset of bounding box regression in order to get accurate proposals. The final proposal layer integrates the foreground anchors and the offsets of the bounding box regression to obtain proposals, and at the same time removes proposals that are too small and beyond the boundary. At this point, the function of target positioning is completed.