## 1 Introduction  
Insulators are widely used in power systems to provide electrical insulation and mechanical support for high-voltage transmission lines [1]. However, under the effects of long-term switching and lightening overvoltage, thermal strain, and natural aging, insulators will fail due to cracks or surface pollution which will hinder their safe operation and cause huge economic losses and casualties in the power transmission system. Therefore, it is crucial to detect insulators from complicated background to ensure the safe running of a power system. With the rapid development of Unmanned Aerial Vehicles (UAV), and since traditional manual inspection methods for detection are time-consuming and highly dangerous, the application of UAV inspection is becoming popular. As the massive aerial images are becoming increasingly easier to access, an accurate and real-time broken insulators location method is urgently needed. At present, the methods for detecting insulators can be divided into two categories, according to the development stage. One is the traditional methods for detecting insulators, which combines human-designed features and classifiers [2; 3; 4; 5]. Another is the the methods of detecting insulators that are based on deep learning networks [6; 7; 8; 9]. For example, in [10], firstly, [PERSON] et al. combined the Canny operator with the SURF algorithm to extract the edge feature points of the image insulator, then used the Haar wavelet to obtain the description information of the feature points and the Euclidean distance ratio to match the feature points of the target insulator, finally using the RANSAC algorithm to obtain the correct matching feature points for realizing the identification and detection of insulators. Although traditional methods provide a new idea for insulator detection, the drawbacks of traditional methods are gradually emerging. The detection results depend on the quality of image segmentation, which seriously affects the speed of subsequent feature extraction and classification and is not very good for insulator image segmentation with complex backgrounds in the traditional detecting methods of insulators. In recent years, since deep learning networks have strong learning and expression ability, and feature extraction is generalized, experts and scholars at home and abroad have gradually applied deep learning networks to insulator detection. There are two main directions in the detecting methods of insulators based on deep learning networks: a single-stage target detection algorithm based on regression problems [5] and a two-stage target detection algorithm based on candidate regions. The main representatives of the single-stage target detection algorithm are the YOLO series algorithm and the SSD algorithm. For example, in [11], an insulator identification method combines traditional methods, such as edge detection and line detection, with the YOLO-V2 algorithm (You Only Look Once V2). However, the design of the loss function and anchor frame of the YOLO-V2 algorithm is far from that of the YOLO-V3 (You Only Look Once V3), and the detection performance is not as good as that of the YOLO-V3. Therefore, in [12], on the basis of the YOLO-V3 algorithm, the focal loss function and balanced cross-entropy function were introduced into YOLO-V3 for the imbalance of positive and negative samples in the training data set. However, since YOLO-V2 and YOLO-V3 are single-stage target detection algorithms, the detection accuracy of single-stage target detection algorithms is lower than of two-stage target detection algorithms. [PERSON] et al. applied the YOLO-v3 target detection network to the localization and recognition of power insulator equipment, providing a new concept for power equipment inspection, but the recognition accuracy is still far from practical applications [13]. Compared with the single-stage target detection algorithm based on regression problems, the two-stage target detection algorithm based on candidate regions is superior in detection accuracy and positioning accuracy.