## 3 Materials and Methods
### Swin Transformer Backbone for Feature Extraction  
A Transformer network is a purely attentional mechanism network that has succeeded significantly in natural language processing tasks. The Transformer network consists of two parts: an encoder and a decoder. The Vision Transformer mainly utilizes the Transformer encoder for feature extraction. In image processing tasks, the image is first sliced into small chunks and then fed into the Transformer encoder, which is capable of outputting correlation information between image chunks. The multi-attention mechanism is the Transformer encoder's core, which utilizes multiple self-attention computations to obtain the correlation information between different sequences. The Swin Transformer, in contrast to the Vision Transformer, considers the computational resources of the network and has shown remarkable performance in various computer vision tasks such as detection, segmentation, and classification. It adopts a layered structure similar to a CNN, dividing the network into four stages. Each stage contains a higher number of feature maps with a reduced size, allowing for the extraction of global features through self-attention and multi-headed attention mechanisms while preserving important features at each scale. As shown in Figure 2, a single STR block is concretely presented. Firstly, the W-MSA (Windows Multi-head Self-attention) module is introduced to restrict the self-attention computation inside a local window, and this local window restriction reduces the computation of the whole network with respect to the global attention computation. The W-MSA module performs the self-attention computation inside each window by dividing the window on the image, which allows the windowed positions to interact with each other for information and to capture local correlations. This localized intra-window attention computation preserves the details inside the window and provides a feature representation with local contextual information. Figure 1: Proposed EYMR-Net framework. However, local window attention computation alone cannot capture the global information of the entire image. To compensate for this shortcoming, the Swin Transformer introduces the SW-MSA module. The SW-MSA module interacts with information between non-overlapping windows through window shift operations. It facilitates attention computation between different windows by cyclically shifting the original feature map so that the windows can be moved to different locations. This attention computation mechanism within moving windows allows for global information exchange between windows, capturing global correlations and dependencies across the image. For example, the shift windows of the Swin Transformer in layer i + 1 comes from the same area of four windows in layer i. That means each window in layer i + 1 is equivalent to the information of four windows in layer i. This overcomes the problem that information cannot be exchanged between different windows. By using both i-th and i + 1 th layers in conjunction, it reduces the number of parameters as required in the traditional self-attention mechanism. Also, it alleviates the problem of the reduced receptive field of the normal window self-attention mechanism. By using the i-th and i + 1 th layers in conjunction, not only can the problem of an excessive number of parameters in the traditional self-attention mechanism be solved, but it also solves the problem of the receptive field of the normal window self-attention mechanism being reduced. In remote sensing image target detection, due to the high image resolution and large differences in the target size and type, it is often difficult for the traditional backbone network to consider the detection effect of targets at different scales. In order to improve the performance of the model in the multi-scale feature extraction of remote sensing images, this study introduces the Swin Transformer structure.