# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 4 Experiments
### Experimental Results  
#### 4.4.1 Comparisons with Other Methods  
In order to directly compare with the original results of the comparison methods, according to the different performance indicators, the method in this paper was tested on three groups and compared qualitatively and quantitatively with methods in other literature works. Data5, Data6, Data15, Data19, Data20, and Data22 in group 1 were used as the test set, and the rest were used as the training set. Data8 in group 2 was used as the test set, and Data2 and Data8 in group 3 were used as the test set. In group 1, the method in this paper was related to multi-scale patch-based contrast measurement (MPCM) [17], multi-scale relative local contrast measurement (RLCM) [46], spatiotemporal local contrast filter (STLCF) [18], and maximum filter, and compared with the four benchmark methods of median filter time fusion (MMTF) [47]. The performance indicators tested were detection rate and false alarm rate. The results are shown in Table 3. The bolded parts in the table indicate the best effect of rearrangement. It can be seen that, in terms of detection rate and false alarm rate, the detection performance of this method on six data segments was greatly improved compared to the three benchmark methods of MPCM, RLCM, and MMTF. Although the false alarm rate on data segment 20 was slightly higher than the benchmark method STLCF, the detection rate of this method was higher than STLCF, and the comprehensive detection performance was also better than STLCF.  
In group 2, the method in this paper was compared with another method from the literature [48]. The performance indicators of the test were detection rate and false alarm  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Data5**} & \multicolumn{2}{c}{**Data6**} & \multicolumn{2}{c}{**Data15**} & \multicolumn{2}{c}{**Data19**} & \multicolumn{2}{c}{**Data20**} & \multicolumn{2}{c}{**Data22**} \\ \cline{2-13}  & \(P_{d}\) **(\%)** & \(F_{a}\) **(\%)** & \(P_{d}\) **(\%)** & \(F_{a}\) **(\%)** & \(P_{d}\) **(\%)** & \(F_{a}\) **(\%)** & \(P_{d}\) **(\%)** & \(F_{a}\) **(\%)** & \(P_{d}\) **(\%)** & \(F_{a}\) **(\%)** \\ \hline MPCM & 68.58 & 4 rate. The results are shown in Table 4. The method proposed in this article was slightly better than the method from the literature [48]. At the same time, the false alarm rate of this method on data segment 8 was reduced by 13% compared with the other method, and the comprehensive detection performance \(F_{1}\) score was 6.84% better than the other method.  
In group 3, the method in this paper was compared with the method in [32]. The performance indicators of the test were accuracy, recall rate, and score. The results are shown in Table 5. On data segment 2 and data segment 8, the accuracy of this method and the _Precision_, \(Recall\), and \(F_{1}\) score were all better than the method in [32].  
The method in this paper was better than the traditional method in terms of real-time performance. A comparison of the inference speed with each method can be seen in Table 6. When the resolution of the input frame was \(256\times 256\), the network inference time of this method was 0.0269, which can meet real-time requirements.