## 3 Textual Method
### PNOC Network Structure  
Through the above DDU network structure, the features of small targets can be directly obtained, and then how to integrate its features well with the features originally used to identify good results is the key to our experiment. Taking into account many methods proposed by previous scholars, the fusion algorithm of this time is intended to achieve an overall consideration. Under the condition of ensuring the stability of the features originally used for identification, the new features were reasonably fused to reduce the negative interference of the new features on the old features. The fusion method designed in this paper adopts the fusion method of attention mechanism, which is very popular now. Common attention mechanisms are divided into channel attention mechanisms, spatial attention mechanisms, and mixed attention mechanisms. The new attention mechanism designed in this paper belongs to the mixed attention mechanism. Considering that the features applied by this attention mechanism are the integration of purified small target features and global features, dimensionality reduction in the traditional attention mechanism should be avoided as much as possible so as to improve the protection of learning channels and further optimize the effect of the attention mechanism on channels. At the same time, because the features processed are highly functional, therefore, regardless of the negative effects brought by efficiency and effectiveness and based on this, the channel attention mechanism network has been designed as follows:  
1. The input features through the global average pooling: the features will evolve from H*W*C matrix to H*W*1 vector. 2. Perform Softplus operation on the obtained H*W*1 vector, thus obtaining the weight of each channel. 3. Multiply the normalized weights and the original input feature channels to generate the weighted feature map. On the one hand, selecting the Softplus curve can alleviate the problem of gradient disappearance; on the other hand, it can make each channel have a weight so as to avoid the existence of a large number of limit values when there is an input of large or small data under extreme conditions. The specific channel attention network is shown in Figure 4.  
Figure 3: Graphic representation of extracting small target feature sets under DDU framework. Different from the channel attention mechanism, where spatial attention is concentrated in the part of effective information, which is a strong supplement to channel attention [35], the specific network construction has been as follows:  
1. Create attention graph \(M_{S}(F)=R*W*H\) using convolutional layers to generate space. 2. Perform two pooling operations to aggregate mapping information of features and record them as \(F_{avg}^{S}\) and \(F_{max}^{S}\), both of which belong to R*H*W, where \(F_{avg}^{S}\) represents average pooling feature and crosschannel feature. 3. Then, using standard convolution layers to connect and convolution, a spatial attention graph of two-dimensional space is generated. In short, the formula is \[M_{S}(F) =\sigma(f^{7\times 7}([ArgPool(F);MaxPool(F)]))\] (8) \[=\sigma(f^{7\times 7}([F_{\text{avg}}^{S};F_{\text{max}}^{S}]))\] (9) where \(\sigma\) denotes the sigmoid function, and \(f^{7\times 7}\) represents a convolution operation with the filter size of \(7\times 7\). The specific structure is shown in the Figure 5.  
The integrated mixed attention mechanism: Through the above channel attention mechanism, we can clearly focus on where, and through the spatial attention mechanism, we can clearly focus on what. How to superposition the two is a problem that needs to be discussed; the common superposition method is the parallel arrangement or sequential arrangement. Through previous experiments, we found that this algorithm is more suitable for sequential sorting, and in the sequential sorting, we found that when the priority of the space lags behind the priority of the channel, the effect is more excellent.