# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
## 4 Experiment and Analysis
### Experimental Results and Analysis
To show the comparison of the detection effects of this method before and after the insertion of the SKNet attention mechanism module, Figure 11 shows the F1-score and F2-score of different categories of this method under the setting of 30-shot. The scores of F1 and F2 in the detection of normal insulators increase by 0.059-0.104 and 0.046-0.075, respectively. Similarly, in the detection of defective insulator parts, the results increase by 0.120-0.156 and 0.056-0.120, respectively. These improvements indicate a broader range of enhancements achieved by the method. The observed improvements can be attributed to the integration of the attention mechanism module into the network. This integration enables the improved network to effectively identify more subtle differences between defects and normal insulators by combining the more detailed features of the insulator image. As a result, the model's identification ability is further enhanced. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **AP\({}_{\text{ms}}\)** \\ \hline Resnet & 0.416 & 0.485 & 0.518 & 0.462 \\ VGG16 & 0.324 & 0.376 & 0.465 & 0.381 \\ Darknet53 & 0.348 & 0.401 & 0.482 & 0.403 \\ SCconv & 0.457 & 0.465 & 0.462 & 0.460 \\ Ours & 0.471 & 0.492 & 0.488 & 0.480 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of insulator detection accuracy of different feature extraction networks. Figure 10: P-R comparison graph. (**a**) 5-shot P-R comparison graph. (**b**) 10-shot P-R comparison graph. (**c**) 30-shot P-R comparison graph. Table 5 shows the average precision rate of different NMS algorithms under two different learning rate decline strategies. As can be seen from the table, under the same NMS algorithm, a higher mAP can be obtained by the warmup cosine annealing strategy, which increases by 0.2-1.5%. Under the same learning rate reduction strategy, the improved multi-stage NMS algorithm effectively strengthens the detection ability of the model, and mAP is improved by 3.6-5.9%. Using the improved method with the combination of the network of NMS and the learning rate decline strategy algorithm, the maximum mAP achieved under the settings of 5-shot, 10-shot, and 30-shot is 29.6%, 36.0%, and 48.3%, respectively. These results demonstrate an improvement of 5.4%, 6.3%, and 4.3% compared to other methods. It shows that the NMS algorithm and warmup cosine annealing training strategy can efficaciously improve the detection ability of the model. Finally, Figure 12 shows the comparison between the improved method and mAP of TFA [16] and the original FSRW method [17] for few-shot object detection. As can be seen from the results, the mAP of the improved method increased by 5.1% and 8.3% in 5-shot settings and increased by 4.2% and 6.1% in 10-shot settings. In 30-shot settings, the results are improved by 8.6% and 11.1%. This significant improvement indicates that the proposed method has a targeted improvement in few-shot insulator detection tasks compared with the baseline method in dealing with few-shot object detection, which verifies the validity of the improved method. Figure 11: Comparison of F1 and F2 scores before and after improvement. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NMS Algorithm** &
\begin{tabular}{c} **Learning Rate Decline** \\ **Strategy** \\ \end{tabular} & **5-Shot** & **10-Shot** & **30-Shot** \\ \hline Hard-NMS & Gradient descent & 0.242 & 0.298 & 0.440 \\ Hard-NMS & Warmup cosine annealing & 0.247 & 0.301 & 0.447 \\ Linear-NMS & Gradient descent & 0.255 & 0.297 & 0.457 \\ Linear-NMS & Warmup cosine annealing & 0.257 & 0.304 & 0.462 \\ Gaussian-NMS & Gradient descent & 0.265 & 0.318 & 0.451 \\ Gaussian-NMS & Warmup cosine annealing & 0.272 & 0.321 & 0.458 \\ Multistage NMS & Gradient descent & 0.281 & 0.350 & 0.468 \\ Multistage NMS & Warmup cosine annealing & 0.296 & 0.360 & 0.483 \\ \hline \hline \end{tabular}
\end{table}