## 2 Materials and Methods
### The Basic Structure and Principle of ED Net
#### 2.2.4 EAC Block  
The convolution operation on an image is essentially a matrix operation; consequently, the convolution operation possesses the characteristics of the matrix multiplication distribution law, in other words, multiple convolution sum operations can be synthesized into a single convolution operation, as shown in Equation (4):  
\[P*k^{(1)}+P*k^{(2)}+P*k^{(3)}=P*(k^{(1)}\oplus k^{(2)}\oplus k^{(3)})=P*K \tag{4}\]  
where \(P\) represents the input feature map. \(k^{(1)}\), \(k^{(2)}\), and \(k^{(3)}\) represent three convolution kernels compatible with size. Equation (4) reflects the fact that the following three operations are equivalent: performing \(k^{(1)}\), \(k^{(2)}\), and \(k^{(3)}\) convolution operations separately on the feature map \(P\) and then adding them, adding \(k^{(1)}\), \(k^{(2)}\), and \(k^{(3)}\) before performing convolution operations with the feature map \(P\), and using a convolution kernel \(K\) matching \(k^{(1)}\), \(k^{(2)}\), and \(k^{(3)}\) to perform convolution operations on the feature map \(P\). In the literature [26], it was mentioned that the skeleton weights of a convolution kernel are usually large and removing these weights can lead to more accuracy degradation than removing the weights in the corners. In other words, the distribution of the learned feature information by the square convolutional kernels is uneven. Therefore, a pair of asymmetric kernels can be added to the corresponding positions of the matched square convolutional kernels for reinforcing the skeleton information as shown in Figure 4. Moreover, it can be concluded from Equation (4) that the square convolutional kernels with reinforced skeleton add no extra computational effort to the network model. Asymmetric convolution block (AC Block) was proposed in the literature [26]. A convolution with inconsistent length and width dimensions of the convolution kernel, such as 3 \(\times\) 1 or 1 \(\times\) 3, is called an asymmetric convolution. The authors replace the single 3 \(\times\) 3 square convolution with another convolution consisting of a pair of asymmetric convolutions and a square 3 \(\times\) 3 convolution, forming a total of three convolution branches in parallel. [PERSON], [PERSON]. [27] proposed an image semantic segmentation method and improved the expression ability of the backbone network based on ACB. [PERSON], [PERSON], [PERSON], et al. [28] proposed a novel self-supervised monocular depth system. Their depth system utilized ACB, which could improve the ability to extract features during training. The asymmetric convolution in the AC Block can improve the network's robustness to image flipping when the network is in the training phase. When the network enters the deployment phase following the completion of training, the three parallel branches respectively perform feature extraction on the image. Then, the batch normalization is carried out and added together. The output is equivalent to a skeleton-enhanced convolution kernel extraction feature extraction result. This process is illustrated in Figure 5 and the literature [26] provides a detailed derivation of the equations for this. Figure 4: The structure of the skeleton-enhanced kernel. In the convolution operation, the main feature information located at the location of the convolution kernel skeleton is emphasized. This design permits fusing the additional feature information extracted by the asymmetric convolutional layers during the training phase of the network without incurring any additional computational effort during the deployment phase. This improves the network's performance when inference is implemented. In this paper, the Enhanced Asymmetric Convolution Block (EAC Block) is constructed based on AC Block. The internal structure of the EAC Block is shown in Figure 6. The EAC Block adds a convolution layer with a convolution kernel size of 1 to each branch and then goes through the smooth-labeled GELU activation function, which further enhances the nonlinearity of the module and enhances its expressiveness.