# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 3 Results
### Qualitative Comparison  
Figures 12-14 show the line segment detection results on images no. 3, 8, and 21 of the MNS-LS dataset, respectively. The image in Figure 12a is a challenging situation, where high-brightness illumination poses a challenge to the detection of the quadrangular structure on the front and makes the detected line segment incomplete. As shown in Figure 12c,d, LSD and EDlines can detect part of the quadrangular structure, but the number of line segments detected by LSD on the solar panel is too small, and it is not robust to the situation of complex illumination, while EDlines causes many complete line segments to break. The orange circular region in Figure 12e shows that Linelet produces many fractures and more false positives than ST_LSD. We can see from the orange circular region in Figure 12f that AG3 line does not perform well in the detection of the quadrille structure on the front and some line segments are fractured. In comparison, as shown in the orange circular region in Figure 12g, ST_LSD can effectively extract some obvious quadrangular structure line segments with high-brightness illumination, and it can maintain the integrity of line segments, such as detecting solar panel line segments while rejecting some false positives.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **F-Score** & **Time/ms** \\ \hline
**LSD** & 0.4994 & 0.3123 & 0.3773 & 43.76 \\
**EDlines** & 0.5761 & 0.3700 & 0.4424 & 47.23 \\
**Linelet** & 0.5782 & 0.3696 & 0.4453 & - \\
**AG3 line** & 0.6432 & 0.4476 & 0.5222 & 27.88 \\
**ST\_LSD** & 0.6807 & 0.4847 & 0.5598 & 33.37 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of different methods on the MNS-LS dataset.  
Figure 13a shows the situation with low-brightness illumination. LSD still has the problem of detecting too few line segments and failing to detect the line segments on the right side of the square surface. EDlines also has the same problem, but the optimized EDlines performs better than LSD. As shown in the orange circular region in Figure 13e, Linelet detects the most fracture line segments and produces the most fractures. AG3 line performs better than the others except for ST_LSD. ST_LSD detects more complete line segments and also rejects some false positives on the square.  
Figure 11: Detailed performance of different methods on the MNS-LS dataset. (**a**) Precision. (**b**) Recall. (**c**) F-score.  
Figure 12: Line segment detection result on image no. 3 of the dataset.  
Figure 14a represents the situation with noise interference. As shown in Figure 14, ST_LSD outperforms the others in rejecting false positives. In addition, ST_LSD detects the largest number of meaningful line segments. Many false positives are generated by LSD, EDlines, and Linelet. LSD and EDlines split a complete line segment into many small lines, while Linelet is found to produce many fractures and more false positives than ST_LSD.  
The quantitative evaluation in Table 2 corresponds to the qualitative results. It can be seen that ST_LSD has good performance in the detection performance of three images. ST_LSD achieves the top score in precision, recall, and F-score. Note that the computational time of ST_LSD ranks second, which is only slightly slower than AG3 line.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**LSD**} & \multicolumn{4}{c}{**EDlines**} & \multicolumn{4}{c}{**Linelet**} & \multicolumn{4}{c}{**AG3 line**} & \multicolumn{4}{c}{**ST\_LSD**} \\ \cline{2-19}  & **\#3**