## 3 Efficient Dynamic Perception Net
### Image Segmentation
#### 3.2.1 LMRC  
The encoder extracts feature information of different levels from the ice-covered transmission line image, which is related to the quality of subsequent ice-covered segmentation image generation. Therefore, the lightweight design of the encoder part is the focus of this paper. Currently, depthwise separable convolution is widely used in lightweight networks. Depthwise separable convolution splits ordinary convolution into depth convolution and point convolution. Each convolution kernel of the depth convolution is only responsible for the calculation of one channel, so its output channel is the same as the input channel number. Since the depth convolution does not change the number of feature channels, resulting in too little feature information of the image, the number of channels is increased by using a 1 \(\times\) 1 convolution kernel of the point convolution. The depth convolution is responsible for extracting spatial information, and the point convolution is responsible for extracting channel information. The number of parameters for ordinary convolution is  
\[Para_{conv}=D_{K}\times D_{K}\times M\times N \tag{1}\]  
\(D_{K}\) is the convolution kernel size, \(M\) is the number of input channels, and \(N\) is the number of output channels. The number of parameters of separable convolution is  
\[Para_{Deconv}=D_{K}\times D_{K}\times M+M\times N \tag{2}\]  
The parameter ratio is  
\[\frac{Para_{conv}}{Para_{Deconv}}=\frac{D_{K}\times D_{K}\times M\times N}{D_ {K}\times D_{K}\times M+M\times N}=N+D_{K}{}^{2} \tag{3}\]  
Therefore, depthwise separable convolution reduces the number of parameters of ordinary convolution, enabling the model to run on edge devices. In addition, asymmetric convolution can also reduce the number of convolution calculations and is more suitable for bar-shaped objects such as transmission lines. The convolution kernel of ordinary convolution is a square such as 3 \(\times\) 3, while the asymmetric convolution kernel splits it into 1 \(\times\) 3 and 3 \(\times\) 1. This asymmetric convolution improves the model's ability to detect bar-shaped objects and reduces the number of model parameters.  
The surface of the ice-covered transmission line is irregular in shape, and convolution kernels of different sizes can extract multi-scale image feature information. This paper uses the ideas of depthwise separable convolution, asymmetric convolution, and multi-scale to design a LMRC block. Figure 2 is a schematic diagram of the LMRC block.  
The following is the pseudocode implementation of Algorithm 2, namely LMRC:The LMRC block consists of two parts. Firstly, the channel is converted through a 3 \(\times\) 3 ordinary convolution, doubling the number of input channels to enhance the richness of features. Then, the features are extracted using a lightweight multidimensional reorganization convolution. A 1 \(\times\) 1 convolution is used to decompose the input channel into four branches with the same number of channels, which then use point convolution to reduce the number of parameters. The asymmetric convolutions of different scales are used to extract multi-dimensional spatial information, and the change of scale is controlled by the dilation rate. Dilated convolution inserts gaps in the intervals of regular convolutions, expanding the receptive field range without increasing the number of convolution parameters. Finally, these decomposed branches are recombined to obtain the output features. The aggregation of feature information at different scales enhances the expressive power of the features.  
The number of parameters for lightweight multidimensional reorganization convolution is  
\[Para_{LMRC}=\frac{(M+8\times D_{K})\times N}{4} \tag{4}\]  
The parameter ratio with ordinary convolution is  
\[\frac{Para_{conv}}{Para_{LMRC}}=4{D_{K}}^{2}N+2D_{K}M \tag{5}\]