# Learning Motion Constraint-Based Spatio-Temporal Networks for Infrared Dim Target Detections
## 3 Results and Discussion
### Performance Comparison and Discussion
#### 3.3.2 Qualitative Evaluation  
Figures 13-18 visualize the qualitative comparison of the proposed model and other SOTA methods. It can be seen that the proposed method achieves an efficient and robust detection in a diversity of backgrounds. Figures 13 and 14 show detection results between the proposed method and traditional single-frame-based methods. In Dataset 1-Dataset 3, the isolated noise points caused by the nonuniformity of the infrared sensor exist in each frame. When the target size turns a few pixels, the local contrast is similar to the isolated noise points. It is difficult to pick out the true target for the traditional methods. Instead, the neural network extracts more stable features of the object. For Dataset 4-Dataset 6, the background becomes complex. Swaying trees and high glossy tarmac roads are present in the frame. The traditional single-frame-based methods are easily confused by them. The false alarm rate is quite high. However, our method still achieves excellent performance. Figures 15 and 16 display the comparison between our method and traditional multiple-frame-based methods. Obviously, compared to single-frame methods, temporal cues of the consecutive frames improve the performance. It helps avoid most static pseudo-targets. However, the fixed parameter model cannot adapt to the random motion of the target. The detection rate, therefore, is not very satisfactory. For the proposed model, the estimation of the spatio-temporal constraint belongs to a flexible learning proce \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Author-Collected Dataset**} & \multicolumn{3}{c}{**Public Dataset**} \\ \cline{2-7}  & **IoU (\(\times\)10\({}^{-2}\))** & **DR (\(\times\)10\({}^{-2}\))** & **FA (\(\times\)10\({}^{-2}\))** & **IoU (\(\times\)10\({}^{-2}\))** & **DR (\(\times\)10\({}^{-2}\))** & **FA (\(\times\)10\({}^{-2}\))** \\ \hline WSLCM [32] & 2.81 & 32.97 & 19.40 & 3.78 & 45.96 & 13.76 \\ \hline MCLoG [33] & 9.78 & 45.35 & 26.78 & 19.78 & 53.16 & 7.04 \\ \hline DP [11] & 60.75 & 66.97 & 7.93 & 73.65 & 77.94 & 17.9 \\ \hline TVF [10] & 59.54 & 61.81 & 17.18 & 65.32 & 75.16 & 8.49 \\ \hline ROLO [30] & 50.32 & 73.97 & 0.732 & 65.08 & 84.57 & 0.45 \\ \hline IRSTD-GAN [40] & 45.68 & 76.31 & 3.45 & 75.32 & 86.16 & 1.38 \\ \hline
**Proposed** & **86.99** & **95.87** & **0.10** & **88.65** & **97.96** & **0.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: We compute IoU, DR, and FA on public dataset and author-collected dataset with different SOTA methods. For IoU and DR, a higher value means better results. However, a smaller value of FA means that the approach works better. The best results are shown in bold. dure. The experimental results show that it could make a more accurate prediction of the moving target. Figure 14: Detection results between our method and traditional single-frame-based methods in public datasets. The target has been marked by texts, and the other prediction boxes are false alarms. Experimental results show that the conventional single-frame algorithm has a high false alarm rate except for the proposed method. Figure 13: Detection results between our method and traditional single-frame-based methods in self-made datasets. The target has been marked by texts, and the other prediction boxes are false alarms. Experimental results show that the proposed method has the highest coincidence with the ground truth. Figure 16: Detection results between our method and traditional multiple-frame-based methods in public datasets. The target has been marked by texts, and the other prediction boxes are false alarms. Experimental results show that only the method in this paper achieves continuous detections. Figure 15: Detection results between our method and traditional multiple-frame-based methods in self-made datasets. The target has been marked by texts, and the other prediction boxes are false alarms. Experimental results show that only the method in this paper achieves continuous detections.