# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 4 Experiments
### Experimental Results
#### 4.4.2 Ablation Experiment  
We conducted ablation experiments to investigate the impact of the actual number of frames \(n\) input into the network and the total number of consecutive frames \(N\) used for frame sampling on the detection results. We obtained \(N\) by multiplying \(n\) by a positive integer \(\mu\), and the experiments were conducted on group 1. The impact of different actual input frame numbers \(n\) on detection performance is shown in the Table 7. According to Figure 8, the \(F_{1}\) score of the model was the highest when \(\mu\) was 5. Therefore the results in the table were all experimentally obtained under the condition that \(\mu\) was 5. As \(n\) increased, the score of the model also increased slightly, but the Flops, model Parameters, and Inference Time of the model increased sharply, and the corresponding index score reflecting the accuracy of model detection only increased to a limited extent. Considering these factors comprehensively, this paper set the actual number of frames input to the network to 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Input Size** & **Inference Time(s)** & **FPS** \\ \hline MPCM & [256,256] & 0.5208 & 1.92 \\ RLCM & [256,256] & 4.1667 & 0.24 \\ STLCF & [256,256] & 1.3513 & 0.74 \\ MMTF & [256,256] & 0.6329 & 1.58 \\ [PERSON] et al. [48] & [256,256] & 0.0596 & 16.78 \\ [PERSON] et al. [32] & [256,256] & 0.0282 & 35.50 \\
**Ours** & [256,256] & **0.0269** & **37.14** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Inference time and fps between our model and other methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Data8**} \\ \cline{2-4}
**Method** & \(\mathbf{P_{d}}\) **(\%)** & \(\mathbf{F_{a}}\) **(\%)** & \(\mathbf{F_{1}}\) **(\%)** \\ \hline [PERSON] et al. [48] & **98.12** & 14.35 & 91.46 \\
**Ours** & 97.80 & **1.2** & **98.30** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of different methods for each data segment in group 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Data2**} & \multicolumn{3}{c}{**Data8**} \\ \cline{2-5}
**Method** & _Precision (\%)_ & _Recall (\%)_ & \(\mathbf{F_{1}}\) **(\%)** & _Precision (\%)_ & _Recall (\%)_ & \(\mathbf{F_{1}}\) **(\%)** \\ \hline [PERSON] et al. [32] & 98.9 & **99.6** & 99.2 & 98.4 & **99.2** & 98.8 \\
**Ours** & **99.3** & 99.5 & **99.4** & **100** & 99.0 & **99.5** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test results of different methods for each data segment in group 3.  
For the proposed feature alignment module, in order to verify its effectiveness, ablation experiments were conducted on each component to verify its effectiveness. The experimental results are shown in Table 9. \(\bigvee\) in the table indicates retaining the module in that column, and \(\bigvee\) indicates removing the module. FAM (feature alignment module) is feature alignment module, which is explained in Section 3.2.1, and the experimental results show that each component in the proposed feature alignment module had a positive impact on the model performance.