# DRUNet: A Method for Infrared Point Target Detection
## 3 Proposed Network
### Target Extraction Module  
UNet++ was inspired by DenseNet [29]. DenseNet uses a dense residual structure as the backbone of image classification to effectively improve the classification accuracy, and this structure is also adopted in UNet++. The advantage of the dense structure is that it can solve the problem that the shallow network is difficult to optimize when the gradient is back-propagated; by feature fusion, the deep network can combine the features of the shallow network and fuse the detailed information of the lower layers and the semantic  
Figure 3: The structure of the ECA-Rest Block is shown in the figure. The input of each block is summed with the original input after a 2D convolution, BatchNorm, and ECA module, and after a ReLU activation layer, we can take the output of this block. Figure 2: This is a diagram of our efficient channel attention (ECA) module. Using aggregated features generated by global average pooling (GAP), ECA generates channel weights by performing a fast 1D convolution of size k, where k is adaptively determined based on channel dimension C.  
information of the higher layers, increasing the perceptual field of the shallow layers. This mechanism can obtain more shallow information, which is well suited for small target detection tasks. For small targets with only a few pixels, it is very easy to lose the most critical target information in the process of up-sampling and down-sampling. Inspired by UNet++, we propose a feature extraction network for small infrared targets, named DRUet, using the dense method that fuses low-level features and high-level features to solve the problem of information loss as the network deepens and the information of small targets is submerged. Additionally, using the ECA-Rest Block can enhance the information of each node in the dense network. The structure is shown in Figure 4.  
Each node contains two operations. After the connection operation receives information from other nodes, we use the ECA-Rest Block for target information self-enhancement, which does not change the size of the feature map and the number of channels. We express this process using Equation (3):  
\[B^{(i,j)}= \begin{cases}b\Big{[}D\Big{(}B^{(i-1,0)}\Big{)}\Big{]},j=0\\ b\Big{[}concat\Big{(}\Big{[}B^{(i,k)}\Big{]}_{k=0}^{j=1},U\Big{(}B^{(i+1,j-1)} \Big{)}\Big{)}\Big{]},j>0^{\prime}\end{cases}  \tag{3}\]  
where \(U(\cdot)\) is up-sampling, \(D(\cdot)\) is down-sampling, \(B^{(i,j)}\) is the output of each ECA-Rest Block, \(concat(\cdot)\) is concat operation, and \(b[\cdot]\) denotes the operation of the ECA-Rest Block. From the structure diagram of DRUNet, except for the outermost node, the interaction between other nodes, the input of the node on the right in the same layer is the sum of the output of all nodes on the left. The output of the node after down-sampling is the input of the node in the lower-right corner, and the input of the node in the upper-right corner is the output of the node after up-sampling. Figure 4: The figure shows a schematic diagram of DRUNet; each node is ECA-Rest Block. The legend on the top right indicates the operations of the different shapes and colors of lines in the structure graph, and the legend on the bottom right indicates the functions and parameters corresponding to these operations. The operations of skip connect and connect are both cat, linking the feature map in the first dimension of the feature map; for example, the shape of feature map A is (batch size, channels_1,H,W), the shape of B is (batch size, channels_2,H,W), and the size of the feature map after connection is (batch size, channels_1+channels_2,H,W). All up-sampling and down-sampling operations in the network are the same. Bilinear interpolation is a common up-sampling method in semantic segmentation networks, because this method has the characteristics of no learning and fast operation, so we chose bilinear interpolation as the up-sampling method. Each up-sampling halves the number of channels and doubles the size of the feature map.