## 2 Transmission Line Ice Cover Monitoring System
### Wide-Domain Vision Convolution Backbone Network  
Traditional CNNs can obtain a larger receptive field by stacking multiple small convolution kernels; however, as the number of layers increases, the computational complexity of the network also significantly rises, leading to the potential issues of gradient vanishing or explosion [11]. To address this issue, this paper proposes a Wide-Domain Vision Convolution Backbone Network that utilizes larger convolution kernels to enhance the network's perception and generalization capabilities, making it better suited for the complex scenarios discussed in this paper. Its overall structure is shown in Figure 3.  
As illustrated, the Wide-Domain Vision Convolution Backbone Network consists of four main stages, each including convolution layers such as Stem, Stage, and Transition. Among these, the depthwise Conv represents depthwise separable convolution, which consists of two parts: depthwise (DW) convolution and pointwise (PW) convolution. Each convolution kernel in DW convolution computes only the corresponding feature map, which may weaken the information exchange between pixel points of different feature layers, potentially leading to precision loss. PW convolution is a 1 \(\times\) 1 convolution that outputs n channels, aimed at compensating for the insufficient feature interaction in DW convolution. Through pointwise multiplication and addition operations, PW convolution can combine information from different feature maps to enhance the interaction between features [12]. Figure 3: Large dynamic kernel aggregation network. Assuming the input feature map size is \(D_{w}\times D_{w}\times C\), the convolution kernel size is \(D_{k}\times D_{k}\times C\), and the output feature map size is \(D_{w}\times D_{w}\times C_{out}\). The parameter count for the standard convolution layer is given as follows:  
\[P_{\text{S tan}\,dard}=(D_{k}\times D_{k}\times C)\times C_{out} \tag{1}\]  
where \(C\) indicates the number of input feature channels, and \(C_{out}\) signifies the number of output feature channels and convolution kernels. Additionally, in depthwise separable convolution, \(D_{K}\) represents the size of the convolution kernel; the depthwise convolution is responsible for filtering and has a size of \(D_{k}\times D_{k}\times 1\), with a total of C kernels, applied to each input channel. The pointwise convolution is responsible for channel transformation, with a size of \(1\times 1\times C\) and also has \(C_{out}\) kernels, applied to the output feature maps of the depthwise convolution. The parameter count for depthwise separable convolution is given by the following formula. \[ \begin{split} P_{Depthwise}&=(D_{k}\times D_{k} \times 1)\times C+1\times 1\times C\times C_{\text{out}}\\ &=D_{k}\times D_{k}\times C+C\times C_{out}\end{split}  \tag{2}\]  
The ratio of depthwise convolution parameters to standard convolution parameters is:  
\[ \begin{split}\frac{P_{Depthwise}}{P_{\text{S tan}\,dard}}& =\frac{D_{k}\times D_{k}\times C+C\times C_{out}}{(D_{k}\times D_{ k}\times C)\times C_{out}}\\ &=\frac{1}{C_{out}}+\frac{1}{D_{k}\times D_{k}}\end{split}  \tag{3}\]  
Based on this, adopting depthwise separable convolution can resolve the increase in parameter count and floating-point operations brought by using larger convolution kernels. The stem layer consists of a 3 \(\times\) 3 convolution with a stride of 2, depthwise separable convolution, and a DW convolution with a stride of 2, which is used to upscale the input image and reduce its dimensions. Specifically, it is shown as follows:  
\[w^{\prime}_{(h/4,w/4,c1)}=D_{s=2}^{3\times 3}\left[f_{s=1}^{1\times 1}(D_{s=2}^{ 3\times 3}(f_{s=2}^{3\times 3}(w_{h,w,c})))\right] \tag{4}\]  
As stated in the formula, where w represents feature weights and D denotes depthwise separation.