# A Few-Shot Object Detection Method for Endangered Species
## 4 Experimental Results and Analysis
### Results of the Experiment  
The experimental results are shown in Table 3. AR-FSOD performs well in the high-shot task, especially in 10-shot and 30-shot, with accuracy up to 60-80%. The results of AR-FSOD and meta-learning-based and fine-tuning-based few-shot object detection methods for each new category of endangered animal detection in the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Panda** & **Crested Ibis** & **Antelope** & **Golden Monkey** & **Alligator Sinensis** \\ \hline
1-shot & 15.4 & 10.3 & 9.4 & 8.6 & 14.6 \\
3-shot & 32.7 & 28.5 & 23.6 & 21.9 & 31.2 \\
5-shot & 57.6 & 44.4 & 38.7 & 36.2 & 56.8 \\
10-shot & 74.8 & 65.0 & 63.2 & 59.8 & 72.5 \\
30-shot & 83.4 & 76.6 & 72.1 & 69.3 & 81.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Few-shot detection performance on ESD. 30-shot scenario are shown in Figure 7. AR-FSOD achieves significant improvement in detection effectiveness in each category relative to the other three algorithms. This indicates that the method in this paper enables the detector to better utilize the existing information and improve the detection performance for endangered species. In order to evaluate the detection performance of the model on endangered animals more comprehensively, this paper carries out a visual analysis of the model network features, and the results are shown in Figure 8. The first row shows the original image of the endangered animal, the second row shows the thermogram obtained by FSCE, and the lower row shows the thermogram obtained by AR-FSOD. In the first column for the \"panda\", the fine-tuning training only shows scattered activation areas with lighter highlights, while the feature map after the attention mechanism is more focused on the center of the object, with darker highlights, showing stronger activation. The \"created_bis\" in the second column also shows dispersed activation areas, most of which are focused on the background area, while the activation positions are more clustered and concentrated on the target area after the attention mechanism module. Comparing the two previous figures, the highlighted areas of \"antelope\" in the third column and \"alligator sinensis\" in the fifth column in the fine-tuning stage of the heat map are skewed to one side, which cannot be fully represented, whereas, through the adjustment of the attention mechanism, we can obtain more complete coverage of the entire heat map. By adjusting the attention mechanism, a more complete heat map of the highlighted area covering the whole object can be obtained. In the fourth column, the highlighted area of \"golden monkey\" in the fine-tuned heat map is more scattered, and even part of it is outside of the object, while the activated area in the heat map improved by this paper is closer to the center of the object, and the coverage is closer to the ideal state. In summary, compared with the feature maps before the improvement, the improved feature thermograms of AR-FSOD are more expressive of the foreground object, which helps the RPN to generate region proposals with higher quality. Figure 7: Comparison of AP50 for different methods. We visualize the detection results of the model and compare them with the FSCE model, which is shown in Figure 9. As shown in the figure, the first line on the left side shows that the final object detection frame, although focused on the center of the object, has low confidence as the coordinates cover only part of the actual object's position due to the object being partially occluded. In the experimental output of AR-FSOD, the right-hand side shows a substantially correct detection frame with improved confidence in the detection. For the second row on the left, \"created_bis\", and the third row on the left, \"antelope\", the FSCE model suffers from misclassification and low confidence due to the similarity of the base classes \"bird\" and \"cow\" in a large amount of data, and the similarity between the object and the sample-rich base class, which causes the network to prefer judging the object as the base class.