# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 4 Experiment and Results Analysis
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Different COCO Methods Have mAP50\% at 10 and 30 Shots**} \\ \hline \multirow{3}{*}{Method} & \multirow{3}{*}{mAP} & \multirow{3}{*}{mAP50} & \multirow{3}{*}{mAP75} & \multirow{3}{*}{mAP} & \multirow{3}{*}{mAP50} & \multirow{3}{*}{mAP75} \\  & & & IS1 & & & & & \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \hline \multirow{3}{*}{TFaw/ fc [6]} & 10.0 & 19.2 & 9.2 & 13.4 & 24.7 & 13.2 \\ \cline{1-1}  & 17 Eaw/cos [6] & 10.0 & 19.1 & 9.3 & 13.7 & 24.9 & 13.4 \\ \cline{1-1}  & 5 & 5.6 & 12.3 & 4.6 & 9.1 & 19.0 & 7.6 \\ \cline{1-1}  & MetaDet [31] & 7.1 & 14.6 & 6.1 & 11.3 & 21.7 & 8.1 \\ \cline{1-1}  & Meta R-CNN [7] & 8.7 & 19.1 & 6.6 & 12.4 & 25.3 & 10.8 \\ \cline{1-1}  & MPSR [33] & 9.8 & 17.9 & 9.7 & 14.1 & 25.4 & 14.2 \\ \cline{1-1}  & FSCE [23] & 11.9 & - & 10.5 & 15.3 & - & 14.2 \\ \cline{1-1}  & FsDetView [16] & 12.5 & 27.3 & 9.8 & 14.7 & 30.6 & 12.2 \\ \cline{1-1}  & IFA-FSOD (ours) & 12.8 & 25.5 & 10.7 & 15.5 & 31.0 & 15.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of object detection under various few-shot conditions on the MSCOCO dataset. FsDetView [16], it increased mAP by 0.3 percentage points and mAP75 by 0.9 percentage points. When \(K\) = 30, compared with the method FsDetView, mAP75 improved by 3.4 percentage points, and at \(K\) = 30, IFA-FSOD achieved the best performance on all indicators. These results indicate that IFA-FSOD achieved good performance on different datasets and can better cope with FSOD, demonstrating good robustness and generalization ability. In order to gain a more intuitive understanding of the performance of each category in different shot environments, we chose the PASCAL VOC dataset. Figure 7 shows the precision of 20 categories in the dataset under 1-, 2-, 3-, 5-, and 10-shot environments. It can be observed that the performance in high-shot environments is generally better than that in low-shot environments under the same category. Specifically, we find that in a 10-shot environment, the detection performance for each class was optimal. In addition, some categories perform well while others may present challenges in FSOD. This also reminds us that in practical applications, we need to pay more attention to the performance differences between categories to better adapt to FSOD. Figure 8 shows the variation in the loss values of the model at each stage on the COCO dataset with increasing iteration times at a learning rate of 0.001. As the iteration progresses, the losses in each stage are continuously optimized, and the final total loss value stabilizes at 0.172. From Figure 8, it can be observed that during the RPN stage, the loss value of the model is very small, which indirectly indicates that our improved RPN effectively enhances the model's ability to generate candidate boxes for the target during training. In Figure 8, total_loss is the sum of all loss items in the model training process. loss_cls is classification loss, and it is used to measure the performance of the model in the classification task; that is, the accuracy of judging which category the detected object belongs to. loss_box_reg is bounding box regression loss, and it is used to adjust the bounding box predicted by the model to more accurately surround the target object. loss_rpn_cls is RPN classification loss, and it is used to measure the accuracy of RPN in distinguishing the foreground (the area containing the object) from the background (the area excluding the object). loss_rpn_loc is RPN localization loss, and it is used to optimize the prediction of these bounding boxes. Figure 7: Performance of each class under different shots.