## 2 Proposed Method
### Insulator Detection
#### 2.1.1 Model Structure  
As we know, deep convolutional networks have shown a huge success in image recognition. To better extract high-dimensional semantic features of the objects, some representative backbone networks, such as AlexNet [52], VGG [53], and ResNet [54], have been developed and validated on public datasets. A comparison of AlexNet, VGG, and ResNet on ImageNet dataset [46] is shown in Table 1. Specifically, the performances of ResNet are superior to that of the AlexNet and the VGG, and they can increase their Top-5 performance by increasing the depth of the network structure. Despite the Top-5 percentage of ResNet50 is only 0.8% lower than that of ResNet101, its computation time and memory usage are almost half of those of the ResNet101. Considering the advantages of the ResNet50, we adopt it to be the backbone of our proposed network. Moreover, we replace the channel of the last convolutional layer in ResNet50 with 1024. Since the filming angles and filming distances are varied in different images, the phenotypes of the insulators can be divided into three scales: small, middle, and large. To ensure each scale of the insulators can be effectively detected, we refer to the work of [44; 56], and then develop a three branches structure in the proposed model to detect insulators with different scales. Figure 2 shows the whole architecture of the proposed network. Moreover, in the work of [55], the ResNet50 is combined with the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Top-5** & **Times (GPU)** & **Weights** \\ \hline AlexNet & 80.3 & 3.1 ms & 238 MB \\ VGG16 & 90.0 & 9.4 ms & 528 MB \\ ResNet18 & 89.9 & 4.6 ms & 44 MB \\ ResNet34 & 91.1 & 7.1 ms & 83 MB \\ ResNet50 & 92.9 & 11.4 ms & 87 MB \\ ResNet101 & 93.7 & 20.0 ms & 160 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pre-trained models for ImageNet classification [55]. header layer of the YOLOv2 to design a new deep convolutional model, which is named as ResnetV2 in this paper. However, the experimental results on our insulator dataset show that it has a poor performance (Please see experiment), which means the features only obtained by ResNet50 cannot effectively represent the characteristics of the insulators. Thus, it is necessary to design a deeper network structure to learn more effective insulator features from shallow layers. To address this challenge, we develop a cascade convolutional structure in each branch to extract high-dimensional semantic features of different scales of the insulators, as shown in Formula (1):  
\[\left\{ \begin{array}{ll}\text{First kernel}&3\times 3\times L\\ \text{Second kernel}&1\times 1\times M\\ \text{Third kernel}&1\times 1\times N\end{array} \right. \tag{1}\]  
where \(L\), \(M\), and \(N\) indicate the channel number of the kernels. From Formula (1), the designed cascade convolutional structure consists of three convolutional kernels. Specifically, the first convolutional kernel (i.e., \(3\times 3\)) is applied to extract finer insulator feature maps in the 8-neighbor region. Then, the second convolutional kernel (i.e., \(1\times 1\)) is adopted to change the channel number for increasing the non-linearity without changing the receptive fields of the convolutional layers. Finally, the third convolutional kernel (i.e., \(1\times 1\)) is used to achieve cross-channel interaction and feature integration. To make the proposed network as an effective one-stage model that is easy to train and can detect insulators in real-time, it is necessary to connect each branch and share the features in different branches. However, with the depth of the network getting deeper and deeper, the insulators could not be effectively detected as so few features cannot indicate the characteristics of the insulators. Therefore, it is unreasonable to only use the feature maps of a layer in the previous branch as the input of the current branch. To accomplish this goal, we take the fact that the shallow convolution layers can provide low-level features of the insulators (color, texture, and shape, etc.).