# Dim Staring Debris Targets Detection Method with Dense Long Trailing Star
## 5 Conclusions  
This paper presents a detection method for staring debris targets in high-density star long-tail scenarios. By performing rectangular fitting on trailing stars, the influence of noise on the grayscale values of the trailing region was reduced. Based on the statistical characteristics of the staring targets, FCN was trained and learned to detect occluded targets. In total, 100 sets of 15 consecutive frames of images generated by simulation were tested, with a recall rate of over 90%. We conducted detection accuracy tests on changing inter-frame intervals and SNR and provided the relationship between indicator parameters and key parameters. When the inter-frame interval is less than 34, the detection accuracy begins to decrease; When the SNR is below 2.5, the detection accuracy drops sharply. Compared with previous literature, this design has the ability to detect and track occluded targets while ensuring an over 90% detection rate and a less than 10% false alarm rate simultaneously, effectively solving the problem of continuous tracking caused by target occlusion in previous methods.  
Conceptualization, J.Y.; methodology, D.H.; software, X.S.; validation, J.Y. and W.L.; formal analysis, D.H. and X.W.; investigation, X.S.; resources, X.S.; data curation, W.L.; writing--original draft preparation, J.Y.; writing--review and editing, D.H.; visualization, X.S.; supervision, D.H.; project administration, J.Y.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study aims to improve and optimize the computational process of the robotic arm and does not involve human or animal studies. Therefore, ethical review and approval were waived for this study.  
This study is not applicable. This study did not involve human studies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Methods** & **Low SNR** & \begin{tabular}{c} **Occlusion** \\ **Detection** \\ \end{tabular} & \
\begin{tabular}{c} **Lightweight** \\ **Computing** \\ \end{tabular} &
\begin{tabular}{c} **Continuous** \\ **Tracking** \\ \end{tabular} \\ \hline
1 & [12] & \(\times\) & \(\times\) & \(\times\) & \(\sur\) \\ \hline
2 & [13] & \(\sur\) & \(\times\) & \(\sur\) & \(\times\) \\ \hline
3 & [15] & \(\sur\) & \(\times\) & \(\times\) & \(\sur\) \\ \hline
4 & [16] & \(\sur\) & \(\times\) & \(\sur\) & \(\times\) \\ \hline
5 & [17] & \(\sur\) & \(\times\) & \(\times\) & \(\times\) \\ \hline
6 & [18] & \(\sur\) & \(\times\) & \(\times\) & \(\sur\) \\ \hline
7 & [19] & \(\sur\) & \(\times\) & \(\sur\) & \(\sur\) \\ \hline
8 & Proposed & \(\sur\) & \(\sur\) & \(\sur\) & \(\sur\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with different literatures (\(\times\): unavailable; \(\sur\): available).  
Data Availability StatementThis research is mainly aimed at the optimization of principle algorithm. Experimental data and results are mainly obtained by repeated experimental statistics. Experimental data can be obtained according to the formula in the paper, and comparison data can be obtained according to references.  
The authors declare no conflict of interest.