# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
## 1 Introduction  
With the continuous expansion and rapid growth of the scale of the power system, the sustainability of reliable operation of the power grid becomes more important. Given the supportive and isolative functions of insulators, their operating status determines the overall safety of the power system's operation [1]. In the inspection task of electrical equipment such as insulators, real-time identification and detection of image samples are carried out by using object detection network models based on deep learning. These models include the faster region-convolution neural network (Faster R-CNN) [2], the single-shot multibox detector (SSD) [3], you only look once (YOLO) [4], etc. Because of its technical advantages, it can replace the manual to realize automatic and intelligent detection, which has obtained some applications and research. In the literature [5], Faster R-CNN was used as the object detection network; the optimal parameters were obtained by adjusting different network layers and comparing the size of the convolution kernel; and sample expansion was carried out by image rotation. Experimental results showed that this method has high detection performance in the identification of transmission line components and defects. Literature [6] improved the anchor point generation method for the Faster R-CNNnetwork to improve the detection effect, aiming at the situation that the insulators of transmission and transformation are difficult to identify due to different image scales and mutual occlusion. Literature [7] proposed a transmission line component detection method that combined the SSD network with the feature pyramid network, which improved the automatic detection accuracy of insulator faults in unmanned aerial vehicle (UAV) power inspection mode. Literature [8] improved the SSD network according to the specificity of images of electrical equipment components in substations. They introduced an additional feature extraction layer, optimized the quantity and proportion of prediction boxes, and improved the soft-penalty non-maximum suppression algorithm. These modifications led to increased detection accuracy of five types of electrical equipment images, achieved through the utilization of transfer learning during the training process. Literature [9] proposed an improved YOLOv3 [10] detection method for insulator foreign bodies. A dense network is designed to replace the original convolutional network through the reuse of multilayer features and the fusion of insulator images, which can improve detection accuracy while reducing false detection occurrences. Literature [11] proposed an insulator self-detonation identification method based on YOLOv4 [12], which designed a modified detection network with a multi-layer information fusion and attention mechanism. The results showed that the detection precision had improved. Most object detection technologies with a wide range of applications rely on a large amount of annotated data to achieve model training and use network models with good performance to achieve sample detection. Therefore, large-scale open-source general data sets such as MS COCO [13] and Pascal VOC [14] have been widely used. However, the confidentiality and particularity of the actual operation process of the power system usually result in difficulty in obtaining large-scale insulator image data sets for training. For example, in extremely bad weather conditions, it is difficult to obtain sufficient clear and usable images by using a manual or UAV, and there are certain security risks. Given the above problems, the object detection method based on the few-shot theory has gradually entered the field of vision of researchers. Literature [15] made use of a data enhancement approach to handle the problem of few-shot.