# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 6 Conclusions  
In this paper, a novel spatial-temporal saliency-based approach was proposed for space debris detection. The proposed approach uses deep learning techniques to output  
Figure 10: Example of the detected centroid on real video sequence \(1\) (top row) and video sequence \(2\) (bottom row). The detected centroid coordinates and missed detections are marked in green circles and red circles, respectively. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Video Number** & **Video 1** & **Video 2** \\ \hline Object name & Iridium-33 and Cosmos-2251 debris & Gaia spacecraft \\ Object size & 36-64 pixels & 6-64 pixels \\ Object speed & 1.48 pixels/frame & 0.16 pixels/frame \\ Tracks type & line & curve \\ Noise type & Gaussian & Gaussian and stray light \\ Frame rate & 30 frame/s & 25 frame/s \\ Frame number & 763 & 788 \\ Frame size & 720 pixels \(\times\) 480 pixels & 360 pixels \(\times\) 360 pixels \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Details of the real video sequences of the space objects. Figure 9: Two real video datasets: All of the frames are collapsed by a max operator. Video 1 includes two space debris with linear tracks. Video 2 includes one space object with a curved track. the saliency maps and compute the centroid coordinates of the space debris from satellite video sequences. The approach achieves end-to-end saliency detection of space debris without multiple steps of traditional methods. Moreover, as the model can generalize well to unseen space debris, due to the self-contained training samples, it does not require motion information from the space debris to be known a priori. First, a lightweight spatial feature network was established to enable the inference model suitable for deployment on onboard devices with limited storage space. Based on the lightweight backbone, a spatial feature enhancement network was created by capturing both the image-level global context and the multi-scale spatial context. The experiment results show that the spatial feature enhancement network can extract more complex semantic features and complete shape features of small objects. Most importantly, a temporal feature extraction network was introduced by establishing the pair-wise relationships among feature elements in consecutive frames. This enables our method to detect space debris with a curved motion track due to the highly correlated temporal information existing in consecutive frames of satellite video. In addition, a public satellite video dataset for space debris detection was created to evaluate the proposed method. The motion speed and direction of space debris and the diameter of space debris and SNR are considered in the dataset. The results of the detection performance experiments show that both the detection probability and the false alarm rate of the space debris extracted by our method are much greater than those of both the single-frame-based method and the multi-frame-based method. In the sensitivity analysis experiment, the best video clip length used for each batch in the training stage is recommended as 4. The ablation study demonstrates that the model size of our method is largely reduced compared to the current saliency detection models. Moreover, we also tested the robustness of the proposed method on the SDD dataset with different SNRs. When the SNR of space debris is approximately 0.1, the centroid error of the space debris extracted by our method does not exceed 0.9 pixels. Finally, we tested our method on two real video datasets with more challenges. It showed that our method can work well in the space background with significant intensity variations, curved motion tracks, and stray light noise. The advantages of the proposed method can be summarized as follows. (1) The proposed method could achieve end-to-end space debris detection without multiple preprocessing steps. (2) It can detect both the linear and curved movements of space debris without a priori information. (3) It can detect space debris with a low SNR of 0.1.