## 1 Introduction  
Target detection in remote sensing images is one of the basic tasks at present, and it can be subdivided into large target recognition and small target recognition under the background of detection [1, 2, 3]. Table 1 illustrates the size limits for size targets. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Min Rectangle Area** & **Max Rectangle Area** \\ \hline Small object & \(0\times 0\) & \(32\times 32\) \\ Medium object & \(32\times 32\) & \(96\times 96\) \\ Large object & \(96\times 96\) & \(\infty\times\infty\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Defining the size of the target (Adapted from ref. [4]). Through a large number of algorithm competitions and experimental data, it can be identified and concluded that the recognition accuracy of small targets is not comparable to that of large targets. For example, in the proposal of the top-level MS COCO (Microsoft COCO) [5] strength segmentation process described in Table 2, \(AP^{S}\) represents the recognition average precision of small targets, while \(AP^{L}\) represents the recognition average precision of large targets. It can be seen that in the comparison of AP detection, small targets are about 2-3 times lower than large targets. In the proposal for the MS COCO instance segmentation challenge, the AP detection tabulation for small targets was 2-3 times lower than for large targets. The detection of small targets is not available in many branch tasks, such as identifying different types of aircraft. In a large number of remote sensing images, we can find that it is not easy to create high-resolution scene photos to detect small or distant objects. Generally speaking, based on the average resolution of 0.5-5 m per pixel, the object to be detected is only a few pixels in size. In the process of sampling at the Neck layer of deep learning, these small pixel objects to be measured are often important features that are ignored and thus cannot be taken into account. Based on such characteristics, we will find that even mature samples such as Mask-RCNN predict that small objects will appear even if they can be identified by human eyes [6]. However, they will still be missed by the system, as shown in Figure 1.  
In the sample prediction of Mask-RCNN in Figure 1, many small targets that can be recognized by the naked eye will be missed, for example, only six can be detected from hundreds of flying birds, which is attributed to multiple factors. On the one hand, there is occlusion among the birds, thus resulting in incomplete recognition. On the other hand, the size of the birds is also a factor. The birds in the image are small objects, and it has been observed in practice that they are not recognized as effectively as larger objects. So, how to improve the accuracy in identifying small objects is an important research topic. Based on the above problems, some scholars, such as Professor [PERSON], proposed to replicate small targets several times to improve poor recognition by increasing the number of training samples through recurring features, and corresponding experiments were set up and achieved good results. However, this method does not fundamentally solve the problem of obtaining fewer features for small target objects using conventional feature extraction methods, so the effect is not stable. Some scholars, such as Professor [PERSON], also proposed setting different styles of measurement boxes and raising the threshold of small targets by reformulating the calculation method of similarity. Indeed, through the efforts of a large number of scholars, they have made good progress in the corresponding fields [7]. However, even if the scheme proposed by scholars has good robustness in the identification of small targets, the effect of application in the fine-grained detection of lower layers is not satisfactory.