## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(AP^{S}\) & \(AP^{L}\) \\ \hline Megvil (Face++) & 0.343 & 0.660 \\ UCenter & 0.326 & 0.648 \\ MSRA & 0.343 & 0.627 \\ FAIR Mask R-CNN & 0.328 & 0.627 \\ Trimps-Soushen-QINIU & 0.310 & 0.610 \\ bhart umd & 0.312 & 0.606 \\ DANet & 0.283 & 0.591 \\ BUPT-Priv & 0.251 & 0.566 \\ DL61 & 0.246 & 0.551 \\ DENet & 0.222 & 0.584 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AP detection results for large and small targets. Fine-grained classification is a small branch of object recognition, which is the subdivision that distinguishes a common category, such as the classification of a dog, the model of a ship, or the identification of aircraft in the same category [8; 9]. Although deep learning has good experimental results for many visual tasks, its application in fine-grained classification is not satisfactory, which is largely due to the above-mentioned inability to effectively come up with enough distinguishing features [10; 11; 12]. Moreover, the transformation of different poses can also obscure many necessary features. In the common fine-grained recognition idea, some scholars think that the fine-grained foreground is very similar, but it will be disturbed by the background, which leads to the problem of recognition. Therefore, their research is on how to separate the foreground and background to a greater extent. Some scholars believe that we should rely on human prior knowledge, that is, to classify the poses that humans are good at. Recognition and machine learning coordinate to form an automatic classification. Some scholars, such as Professor [PERSON] and [PERSON], are also committed to the pooling layer of machine learning, thus hoping to use higher-order pooling to improve the accuracy of large recognition. With the popularity of the Transformer, the attention mechanism becomes hot, constantly focusing on the next feature point based on the existing feature, to carry out detailed identification [13]. In response to the aforementioned technical issues, this article proposes to design a new network structure DDU (Downsample Difference Upsample) to enhance the features of small targets, thus fundamentally solving the problem of recognition difficulties. DDU is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. The enhanced network approach is based on the effect of upsampling and downsampling to perform appropriate differentiation, thereby achieving the effect of shielding irrelevant features and focusing on necessary features. At the same time, in order to prevent the blocked features from having other factors that are conducive to recognition, the existing attention mechanism has been improved, thus forming a new attention mechanism PNOC (preserves the number of channels). In the dimension of channel attention, the effect of avoiding the common two full connections without losing the  
Figure 1: Examples of identifying small targets with a large number of missing targets. number of channels has been achieved. While achieving feature fusion, the disadvantage of avoiding key features caused by dimensionality reduction has been avoided. We have made the following improvements and extensions in this article:  
1. In the network framework of the Neck layer, the DDU network has been designed, which mainly obtains the features of small targets based on up- and downsampling, thus enhancing the recognition features of small targets, further improving the feature richness of recognition, and effectively solving the problem of low recognition accuracy of small targets. 2. This article also utilizes the improved attention fusion method PNOC to integrate the small target features extracted by the DDU module with the original feature targets sampled without differential operation, thereby making the data usable. 3.