# A Few-Shot Object Detection Method for Endangered Species
## 4 Experimental Results and Analysis
### Results of the Experiment
Taking Pascal VOC Split1 as an example, when the number of new class support images increases from one to five, the AP50 improves by 17.5%; however, when the number of support images increases from five to ten, the AP50 only improves from 63.7 to 67.5%, which shows a non-linear growth. This phenomenon mainly stems from the limited number of samples at the fine-tuning stage, and the model fails to effectively utilize the support set information. However, with the gradual increase in the number of samples, the advantages of the attention mechanism and the weighted prototype comparison branch gradually appear, and the recognition rate of the new category increases significantly. In addition, a visual comparison between AR-FSOD and FSCE models is carried out in this paper, and the results are shown in Figure 10. For \"bus\", which is a small sample category, it is more difficult to detect because of its high similarity with the base class object \"car\". In the first row of results on the left, although the final object detection frame is located in the center of the object, the coordinates only cover part of the actual object, and there are classification errors and low confidence. In the experimental output of AR-FSOD, the detection frame is basically correct, and the correct classification is achieved. For the second row of \"cow\", there is an obvious classification confusion problem, in which the object in the image has similar features to the base class \"horse\", causing the network to prefer to judge it as the base class. After adjusting the final visualization threshold, the detection frame of the corresponding object can be obtained, but its confidence level is only 0.49, which is not up to the standard of correct recognition. In contrast, in the experimental results in this chapter, by adjusting the regression coordinates, the model is closer to the actual labeling and improves the classification confidence. The feature heat map of FSCE in the third column shows that the model activates more background regions, which causes the model to have missed detection. The feature heat map of AR-FSOD, on the other hand, focuses on the object, which can effectively avoid the situation of missed detection. The improved feature extraction network in this model effectively enhances the ability of multi-scale feature extraction. Meanwhile, the attention mechanism module is able to generate more expressive feature maps to a certain extent, which reduces the case of missed detection. In addition, the weighted prototype comparison branch effectively solves the problem of classification confusion. AR-FSOD exhibits better generalization performance and robustness. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Novel Split1**} & \multicolumn{4}{c}{**Novel Split2**} & \multicolumn{4}{c}{**Novel Split3**} \\ \hline
**Method** & **1** & **2** & **3** & **5** & **10** & **1** & **2** & **3** & **5** & **10** & **1** & **2** & **3** & **5** & **10** \\ \hline Meta FR-CNN [30] & 41.8 & 46.7 & 52.7 & 59.6 & 62.3 & 26.1 & 33.6 & 43.8 & 47.8 & 50.1 & 35.6 & 42.1 & 45.8 & 53.4 & 52.3 \\ SRR-FSD [31] & 47.8 & 50.5 & 51.3 & 55.2 & 56.8 & 32.5 & 35.5 & 39.1 & 40.8 & 43.8 & 40.1 & 41.5 & 44.3 & 46.9 & 46.4 \\ MPSR [32] & 36.5 & 41.7 & 48.9 & 54.5 & 60.7 & 25.4 & 27.2 & 39.5 & 41.1 & 46.2 & 35.3 & 42.2 & 44.5 & 49.6 & 50.4 \\ FSCE [22] & 44.2 & 43.8 & 51.4 & 61.9 & 63.4 & 27.3 & 29.5 & 43.5 & 44.2 & 50.2 & 37.2 & 41.9 & 47.5 & 54.6 & 58.5 \\ FSRW [18] & 14.8 & 15.5 & 26.7 & 33.9 & 47.2 & 15.7 & 15.3 & 22.7 & 30.1 & 40.5 & 21.3 & 25.6 & 28.4 & 42.8 & 45.9 \\ AR-FSOD & 45.6 & 45.3 & 53.9 & 63.7 & 67.5 & 27.8 & 30.9 & 44.3 & 46.8 & 53.5 & 38.1 & 43.3 & 49.6 & 58.1 & 61.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fine-tuning results of the new Pascal VOC.