# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
## 4 Experiment and Analysis
### Experimental Results and Analysis  
Figure 9 shows the comparison of insulator test diagram samples of large-scale samples, small-scale samples, defect samples, block samples, samples with salt-and-pepper noise, samples with Gaussian noise, and samples with cutout data enhancement under the 30-shot setting. The first column is the test result for the original FSRW model. The second is the test result of the improved model. As can be seen from Figure 9a, the improved method's positioning ability on large-scale insulator targets is improved by a large margin compared with the original FSRW model. From Figure 9b, the improved model not only improves the positioning ability of small-scale insulator targets but also reduces the occurrence of missed detection. From Figure 9c, the improved method avoids a range of false detections on the whole string of insulators for multiple strings and can better identify the defective part of the insulator. From Figure 9d, compared with the original method, this method can better detect the location and classification information of blocking insulators. Figure 9e,f show that the detection accuracy of this method is still improved after the influence of salt-and-pepper noise and Gaussian noise is introduced, which proves the noise suppression performance of this method. Figure 9g shows that insulator targets can also be detected by this method after cutout rectangular frame occlusion is introduced. Thisproves that this method can also realize positioning and recognition by using a small part of the insulator's characteristic information after occlusion. Figure 9 shows the effectiveness of the improved model. Figure 9: _Cont._  
Figure 10 shows the P-R graph under different shot settings before and after the improvement, in which the abscissa is recalled and the ordinate is precision. As can be seen from the figure, the improved model has a larger area enclosed by the horizontal and vertical coordinates. This indicates that this method has achieved better detection performance in few-shot insulator detection tasks, especially in the settings of 10-shot and 30-shot. Figure 9: Comparison of insulator detection. (**a**) Comparison of large scale insulator detection; (**b**) Comparison of small scale insulator detection; (**c**) Comparison of insulator detection with the defect; (**d**) Comparison of insulator detection with blocking; (**e**) Comparison of insulator detection with salt-and-pepper noise; (**f**) Comparison of insulator detection with Gaussian noise; (**g**) Comparison of insulator detection with cutout rectangular frame occlusion. In order to visually showcase the enhancement brought by the improved self-calibrated feature extraction network in detecting few-shot insulator images of various scales, the backbone network in this model is replaced with different architectures. Specifically, Resnet [27], VGG16 [28] from Faster R-CNN, CSP Darknet53 [12] from YOLOv4, and SCconv are utilized as alternative backbone networks in the model. Under the setting of 30-shot, the detection accuracy results of different feature extraction networks at different scales are shown in Table 4. According to the table, the APs of this method is increased by 1.4-13.3% when detecting insulator targets on a small scale, which proves that the performance of this method is improved. The APM of the proposed method also increases by 0.7-11.6% when detecting medium-insulator targets. When detecting large-sized insulators, the APL of this method is slightly lower than that of Resnet, but it exceeds most feature extraction networks, increasing by 0.6-2.6%. The multi-scale accuracy APms of the feature extraction network improved by this method is increased by 1.8-9.9% when detecting insulators with different boundary frame areas, which shows the validity of the improved method.