# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 2 Methods
### Local Zoom Input  
The fundamental principle in object detection is based on enhancing target features while simultaneously suppressing background noise. In deep learning, this involves strengthening the feature representation of the target and reducing the focus on background features, all within the constraints of limited computational resources. The vision focus mechanism, which mimics the selective focus used by organisms when processing external information, plays a crucial role in this process. By isolating key information relevant to the area of interest or specific objectives from the global context, vision focus mechanisms improve the model's discriminative power. The core of this method lies in extracting essential features related to the task at hand from a large pool of data and amplifying their importance by adjusting the dimensions of the original network input, thereby improving the model's performance.  
As shown in Figure 2, the local branch input divides the image into four quadrants, with each quadrant representing a potential area for applying focus magnification, simulating the biological vision focus mechanism. This process enhances the capture of detailed information from dim and small targets more effectively. The vision process is divided into two stages at the feature extraction layer. In the initial stage, the system acquires various features in an unbiased manner, without incorporating vision focus mechanisms. In the next stage, a series of integrations are performed on the features obtained from  
Figure 1: Improved YOLOv7 network structure diagram. The numbers below represent the size of each feature map. The CBS module is the basic component of the model, consisting of the conv module and the BN module.  
the first stage to form a cognitive representation, which then becomes the focus of visual attention mechanisms. This two-stage process ensures efficient feature extraction without compromising the pixel definition of the original image.  
The selected local area for applying vision focus is randomly determined in the initial frame of the input image. This selection is subsequently updated based on the output of the previous frame for the next frame. The spatio-temporal information derived from the image is used to update the magnified region. Given the weak characteristics of the target, we employ the double cubic interpolation (BiCubic) algorithm to zoom in on the selected region which is then input into the model. Equation (1) shows the above process:  
\[ZF(x,y,z)=zoom(F(x,y,z),C) \tag{1}\]  
where \(zoom(\cdot)\) represents the bicubic interpolation magnification function applied to a selected quadrant \(C\), and \(F(x,y,z)\) denotes the processed frame. The target's coordinates \((x,y)\) are used to determine the relocation of quadrant \(C\), where the vision focus is applied. This design, which significantly enhances the local area, acts as an extension of the vision focus mechanism. It magnifies both the overall and detailed information of the target object of interest while enhancing its spatial location information, making the model more sensitive to dim and small targets.