# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 1 Introduction  
With the continuous development of space technology and the increasing demand of human beings for space exploration, space detection has gradually become a research hotspot in the field of spaceflight [1]. Within this topic, space target detection is the first part of space exploration, through which space targets are detected and recognized to obtain the types and orbital positions of space targets, providing rich information for subsequent orbiting and mission planning [2]. In space target detection missions, targets with large size and signal-to-noise ratios are easily detected. However, for small space targets, such as asteroids [3; 4; 5] and space debris [6; 7], the presence of a star map background in deep space and poor lighting conditions make the imaging area in the image smaller and weaker in energy, which brings difficulties to target detection. In recent years, spatial small target detection has gained broad attention and there are a large number of related research results. In a single-frame image, spatially small targets mostly appear as low-intensity light spots and are imaged in a small area with only a few pixels in the image. Feature-based methods such as scale-invariant feature transform (SIFT) [8], histograms of method gradients (HOG) [9], and the local contrast method [10] can enhance the target information while suppressing the interference of background clutter, but for the task of detecting small targets in space, the existing methods' detection capabilities are weak. To address this problem, researchers have proposed a detection method based on sequential images, where the information about the target in the imageis obtained by processing multiple frames. [PERSON] et al. [11] used the matched frame difference method to detect small spatial targets and obtained small targets in starry sky images by differentiating the sequential frames and then using a mathematical morphology filtering algorithm to eliminate irrelevant data. [PERSON] et al. [12] proposed an improved optical flow algorithm to estimate an image sequence. [PERSON] et al. [13] proposed a multi-stage quasi-hypothesis testing (TMQHT) method to achieve spatial debris detection under low signal-to-noise ratio conditions through time-indexed filtering and multi-stage time indexing. However, all of the traditional methods face the following challenges: firstly, the robustness of traditional methods for target detection under illumination and background interference is poor, and secondly, the existing detection methods need to be executed on each pixel of each image in a sequence of images, and thus, the computational cost is high. With the rise of artificial intelligence, deep learning has been successful in several fields [14; 15; 16], among which computer vision has become a key area of research, and more and more studies are now focusing on spatial target detection. [PERSON] et al. [17] proposed a spatial dark and weak small target detection and tracking method using bilateral filtering and a long short-term memory network. [PERSON] et al. [18] utilized the YOLO network trained on self-constructed astronomical datasets for galaxy detection and classification. [PERSON] et al. [19] proposed an improved Resnet-50 and feature pyramid network under the Faster R-CNN framework for spatial target detection and classification. [PERSON] et al. [20] proposed a channel and spatial attention CSAU-Net network for real-time dark space target detection and segmentation based on spatial image features. However, the existing methods do not consider the effects of different illumination and image signal-to-noise ratio conditions on the detection of small targets in space, and this also poses a great challenge to the detection of small targets in space due to the similarity of their intensities with the energies of the background stars.