## 3 Materials and Methods
### The Location Feature Extraction Strategy  
#### 3.2.1 Location Feature Extraction Structure  
The network structure of the location extraction module (referred to as the LFE module) is shown in Figure 1. Based on the idea of CSPNet, this module was divided into two parts. Part I: First, input features go through three times ConvvNHS module to downsample. ConvNHS is a convolution module including convolution, normalization [48], and the _Hard Swish_ activation function [54]. Normalization accelerates convergence and prevents overfitting. The _Hard Swish_[54] function improved the learning ability of network nonlinear representation without greatly increasing the computational cost. The global spatial context mapping was established through three different dimensional convolution modules. After that, the feature map entered the attention module, including channel attention and spatial attention. Because the location feature extraction of the channel attention mechanism was not strong [32], the spatial attention in the attention module was used to obtain more spatial information. In addition, the cross-channel information interaction of the ConvNHS module can make up for the lack of spatial information in channel attention to a certain extent. Part II: The second branch retained the initial fine-grained feature information to avoid the loss of global spatial information. The later experiments also proved that this structure does reduce the location errors and achieve the location feature extraction process (Figure 2). Figure 1: Self\(-\)supervised self-distillation module: Simdis2x. Figure 2: Location feature extraction structure. ConvNHS is a convolution block that contains convolution, normalization, and the _Hard Swish_ activation function. CAM: channel attention mechanism. SAM: spatial attention mechanism. CBL, a convolution module, includes convolution, batch normalization, and the _LaukyReLU_ function. The specific model architecture is as follows: The feature map (H \(\times\) W \(\times\) C1. H, W: height and width of the feature map. (C1: number of feature map channels) entering the module is processed in two branches. Branch 1, the ConvNHS module, including convolution, normalization, and activation functions, was written in _Hard Swish_. This module used convolution kernels of different sizes to change the number of channels, achieving cross-channel information interaction and the establishment of spatial context mapping. The normalization accelerated convergence, and _Hard Swish_ completed the nonlinear modeling of input in a deeper network. Under the condition of keeping the original size of the feature map, the number of channels of the feature map was changed to C2, so the cross-channel information interaction was realized. Under the condition that the amount of parameters does not increase significantly, the ConvNHS module enhanced the learning ability of nonlinear features and enhanced the learning of the location relationship in the deeper network. The problem that location feature extraction was not strong in the next channel attention mechanism was resolved, and a feature map \(F_{context}\) was obtained. \[F_{context}=\text{ConvNHS}(F), \tag{5}\]  
Next, it was the channel attention mechanism: two one-dimensional feature maps were obtained through global max pooling and global average pooling, and a channel attention vector was obtained through a multi-layer perceptron (MLP) and activation function processing. With the previous ConvNHS module processing, the channel attention mechanism could interact with more cross-dimensional information and enhance the attention to spatial information. \[F_{c,context}=V_{c}\otimes F_{context}, \tag{6}\]  
Here, \(F_{c,context}\) is different from the general channel attention feature map, which is the feature map that obtains interactive information and spatial context mapping. \(\otimes\) denotes element-wise multiplication. Deploy spatial attention after channeling attention to enhance attention to spatial information.