# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
## 4 Experiments
### SMG-C Results  
We use SMG-C as the detection framework and implement experiments on both FLIR and IAT benchmarks. The baseline method in this subsection is the original CenterNet [19] without SMG.  
Table 3 shows the comparison of AP for each class and mAP of SMG-C against the baseline detection network when trained with different numbers of training examples on the FLIR benchmark. One can see that our SMG-C outperforms the baseline detector across all classes when trained with the same dataset. For example, SMG-C on FLIR obtains 75.6% mAP, which is 4.5% higher than the baseline. This can be attributed to the fact that the source model offers sufficient guidance for the infrared detector in SMG.  
More importantly, SMG-C achieves outstanding performance when the training data are insufficient. Taking the bicycle as example, we find that its AP maintains 51.5%, although the training examples are reduced to 1/4 of the original. In contrast, the highest bicycle's AP is 51.2% for the baseline method. Furthermore, the mAP of SMG-C trained on FLIR-1/2 obtains 73.3% mAP, surpassing the original CenterNet trained on the entire FLIR (71.1%).  
We also report the results on the IAT benchmark in Table 4. All mAPs of SMG-C exceed 95%, while the highest accuracy of CenterNet is only 93%. When we reduce training datasets to half of the original, the accuracy of the baseline drops to 90.6%, while SMG-C maintains 95.2% in mAP. Furthermore, SMG-C trained on IAT-1/2 surpasses the baseline method trained with the entire training dataset. This demonstrates that SMG-C yields an effective infrared detection method even when there are a lack of available training data.  
Some results on IAT-1/2 are visualized in Figure 7. When the target is small, some interference from the background may adversely affect the detection especially in the absence of enough training examples. As shown in Figure 7, the baseline CenterNet hardly overcomes this problem so as to generate many wrong detection results. However, SMG-C guided by the high-performance RGB model suppresses the interference from the background and predicts more precisely than the baseline.  
\begin{table}
\begin{tabular}{l l r} \hline \hline
**Dataset** & **Method** & **mAP (\%)** \\ \hline \multirow{2}{*}{IAT} & Baseline & 93.0 \\  & SMG-C & 96.8 \\ \hline \multirow{2}{*}{IAT-1/2} & Baseline & 90.6 \\  & SMG-C & 95.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection results of SMG-C on the IAT benchmark.  
\begin{table}
\begin{tabular}{l l r r r r} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{3}{c}{**AP (\%)**} \\ \cline{3-6}  & & & **Person** & **Car** & **Bicycle** \\ \hline \multirow{2}{*}{FLIR} & Baseline & 71.1 & 76.6 & 85.4 & 51.2 \\  & SMG-C & 75.6 & 79.0 & 85.8 & 62.0 \\ \hline \multirow{2}{*}{FLIR-1/2} & Baseline & 68.1 & 75.1 & 83.5 & 45.8 \\  & SMG-C & 73.3 & 78.7 & 86.0 & 55.3 \\ \hline \multirow{2}{*}{FLIR-1/4} & Baseline & 65.8 & 71.5 & 81.8 & 44.1 \\  & SMG-C & 70.9 & 76.7 & 84.5 & 51.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection results of SMG-C on the FLIR benchmark.