# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 1 Introduction
(3) Various noises exist in space surveillance platforms, such as thermal noise, shot noise, dark current noise, and stray light. Therefore, space debris energy is very faint compared to the noise background, which also brings challenges to space debris detection. To solve these difficulties, we propose a novel spatial-temporal saliency method called SDebrisNet for detecting space debris in satellite videos; it uses lightweight atrous spatial \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Satellite Name** & \begin{tabular}{c} **Field of View** \\ **(°)** \\ \end{tabular} & \
\begin{tabular}{c} **Resolution** \\ **(Pixel \(\times\) Pixel)** \\ \end{tabular} &
\begin{tabular}{c} **Frame Rate** \\ **(Frames/s)** \\ \end{tabular} \\ \hline UtheCast [29] & - & 1920 \(\times\) 1080 & 3 \\ TUBSAT [30] & 0.28 \(\times\) 0.21 & 752 \(\times\) 582 & - \\ SkySat [31] & - & 1920 \(\times\) 1080 & 30 \\ Tiantuo-2 [28] & 2.5 \(\times\) 2.5 & 960 \(\times\) 576 & 25 \\ Jilin-1 [29; 32] & - & 3840 \(\times\) 2160 & 25 \\ \hline \hline \end{tabular}
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Table 1: Typical video satellites and key parameters. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Methods** & **Advantages** & **Disadvantages** \\ \hline Single-frame-based method & It is straightforward. & (1) It needs a priori information and many predefined templates. (2) The faint and moving objects could not be detected. \\ \hline Multi-frame-based method & The faint and moving objects could be detected. & (1) It is time-consuming. (2) It needs a priori information. \\ \hline Video-based method & (1) The faint and moving objects could be detected. (2) It does not need a priori information. \\ \hline \hline \end{tabular}
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Table 2: The advantages and disadvantages of different space debris detection methods. pyramid pooling and scale-enhanced structures to enhance context-aware and scale-aware features, while constrained self-attention helps capture local temporal features. First of all, a spatial feature extraction module (SFM) is proposed to improve the feature extraction performance for small objects. We added a high-resolution feature detector to the multi-scale feature extraction network to extract more specific features from the low-level feature map. Then, the spatial data of small objects were further strengthened by a spatial feature enhancement module (SFEM). Next, the spatial-temporal coherence was enhanced by a temporal feature extraction module (TFM). Finally, the saliency map of space debris can be obtained by a spatial-temporal feature fusion module (STFM). The main contributions of this paper can be summarized as follows. (1) In order to account for the unique characteristics of moving space debris in satellite videos, a spatial-temporal saliency framework was developed. This framework enables end-to-end space debris detection without the need for preprocessing or post-processing steps. (2) SFM extracts the spatial features using a lightweight neural network, which is well-suited for space-based computational platforms with limited storage and computational resources. (3) To deal with the small object detection problem, SFEM enhances both the context-aware features and the scale-aware features, which could significantly improve object detection precision in different scales, including small objects. (4) TFM connects both the spatial features and temporal features from the consecutive frames. Even if space debris data possess a low signal-to-noise ratio, our method could effectively output the saliency maps. The novelty of this paper can be summarized as follows: (1) A new space debris detection method based on spatial-temporal saliency is proposed. (2) A new saliency detection neural network based on the constrained self-attention is proposed. (3) A new small object feature extraction network, including a spatial feature extraction network and spatial feature enhancement network, is proposed. The rest of the paper is organized as follows.