## 2 Related Work  
As a hot research topic in the field of computer vision, small target detection has been proposed by many scholars in recent years, which can be divided into the following methods. Firstly, feature learning based on different dimensions is mainly aimed at solving the problem of small targets with few features. Based on this, scholars have put forward feature pyramid-based and receptive field-based methods, in which feature pyramid-based methods are represented by FPNs (Feature Pyramid Networks). The main idea is to combine the low-level and high-level semantics with more spatial information to enhance the characteristics of the target [15]. The receptive field-based approach is represented by the Trident Network, and the idea is to assign different receptive fields based on the size of the target. Specifically, the dilated convolution with different dilation rates is used to form three branches of objects with different receptive fields that are responsible for different scales [16]. Secondly, some scholars put forward the GAN-based method, which solves the problem that the small target itself has few identification features, and its thinking and practice are also simple and plain: using GANs (Generative Adversarial Networks) to generate high-resolution pictures or high-resolution features. They are not limited to SOD-MTGAN methods for generating high-resolution features, and the general idea is to use the trained detector to obtain subgraphs containing targets. For example, Faster R-CNN then uses the generator to generate the corresponding high-definition image, and based on the discriminator, it is responsible for whether the generated image is real or fake [17; 18; 19]. At the same time, this part is carried out from the detector to further predict the category and position of the object. Common ways to generate high-resolution features are Perceptual GANs and BFFBB GANs (Better to Follow, Follow to Be Better Generative Adversarial Networks) [20; 21]. Its core ideological history takes the input image shrunk by two times and the features extracted through feature extraction as the low-resolution feature and the features extracted from the original image as the corresponding real high-resolution feature; the generator based on a low false high-resolution feature is generated by the resolution feature. The discriminator is responsible for distinguishing the false and true high-resolution features. Thirdly, some scholars put forward context-based thinking, which uses the dependency between the environmental information of small objects and other easily detected objectsto enhance detection visibility. The essence of problem solving is to make up for the lack of features of small objects [22; 23]. Common methods are inside-OutsideNet and Pyramid box, where inside-OutsideNet extracts information about the global context of an object through four inverted RNN (Recurrent Neural Network) pyramid boxes [24; 25]. Pyramid box aims to use the prior information that the head and body always appear together with the face, because the first two are relatively easier to detect than the face, and then use the detected head and body to position the face for further detection. The highly dependent relationship is based on the currently hot attention mechanism Transformer combined with implicit modeling of the relationship between two objects and the use of this dependency relationship to strengthen the characteristics of each objective target. Next, some scholars have proposed certain solutions for specific datasets, but this method is only useful for specific datasets, such as MS COCO datasets. In image-level and instance-level imbalance, the image-level imbalance is that only 51.8% of pictures in MS COCO have small objects [26]. Therefore, the outcome is to set a Stitcher as the feedback signal for the proportion of loss of small objects in the total loss. When the proportion is lower than the threshold, the four pictures are stitched together into one picture for input, thus increasing the number of small objects.