# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 5 Experimental Results and Analysis
### Ablation Study  
To investigate the effectiveness of SFM, SFEM, and TFM modules in SDebrisNet, the ablation experiment is conducted in this section. To analyze the contributions of individual modules, the saliency detection results of SDebrisNet without different components are shown in Figure 8 and the corresponding quantitative results are recorded in Table 6. As shown in Figure 8, ground truth displays the clearest and strongest streak among all of the detection results. SFM reveals a sparse streak; this is because SFM cannot extract sufficient spatial features due to the tailored feature extraction network. SFEM performs better than SFM due to the more enriched spatial features learned by SFEM. Since the temporal feature extraction module is added on the basis of the original feature extraction network, TFM also performs better than SFM. The performances of (f) and (g) are slightly better than (d) but significantly better than (e). It is because the spatial features and the temporal features are captured by the network. Moreover, the SDebrisNet performs better than (f) and (g) and the best in the ablation study. The reason is that the SFM+SFEM extract the strong spatial features while the TFM learns the motion cues simultaneously. In Table 6, the first row represents the detection results of the baseline composed of original MobileNetV3+SPM, which achieves poor detection results in terms of both \(F_{\beta}\) and \(CE\) due to insufficient spatial-temporal features extracted from the baseline network. The performance is degraded when MobileNetV3 is replaced by SFM due to the tailoring of the original network structure. After adding SFEM, it can create a certain level of performance improvement, which is consistent with the results shown in Figure 8. On the basis of SFM or SFEM, adding the TFM can significantly increase by 15.5% and 16.6% w.r.t \(F_{\beta}\), respectively.  
The model size is a key metric that is limited by the onboard computers with restricted storage resources. As shown in Table 6, compared to the original model, the outcome of the designed SFM reduced the model size by 5.4 MB with almo  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & \(P_{d}\uparrow\) & \(P_{f}\downarrow\) & **Running Time** \\ \hline _singleframe-based method_ & & & \\ CSAU-Net [57] & 0.912 & 0.021 & 0.021 s \\ _multiframe-based method_ & & & \\ TS [58] & 0.933 & 0.035 & 0.074 s \\ _video-based method_ & & & \\ NODAMI [28] & 0.961 & 0.028 & 0.195 s \\ SDebrisNet & **0.996** & **0.011** & **0.013** s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection results of different space debris detection methods on the SDD dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \(T\) & \(Precision\uparrow\) & \(Recall\uparrow\) & \(F_{\beta}\uparrow\) & \(MAE\downarrow\) \\ \hline
2 & 0.733 & 0.805 & 0.982 & **0.014** \\
4 & **0.802** & 0.796 & **0.984** & **0.014** \\
6 & 0.771 & **0.821** & 0.963 & 0.015 \\
8 & 0.707 & 0.798 & 0.976 & 0.016 \\
10 &Besides, the memory overheads of all the models are no more than 100 MB, which fits within the supported maximum synchronous dynamic random-access memory (SDRAM) of 512 MB for the current onboard computers [42]. This shows our model's superiority to state-of-the-art saliency detection models with several hundreds of megabytes [34; 45] when deployed on SDRAM-limited devices.  
We compared the proposed method with the recent saliency detection models in terms of model size and computational efficiency. The results are listed in Table 7. We can see that the current saliency detection models mainly take the ResNet as the backbone to extract the spatial features. The model size of the proposed method is no more than 90 M and achieves a good inference speed among state-of-the-art saliency detection models.