###### Abstract
Therefore, one-stage networks, represented by You Only Look Once (YOLO) [26, 27, 28, 29] and Single Shot multi-box Detector (SSD) [30], came into being. One-stage networks are end-to-end detection methods which can predict the classification probability and position information simultaneously. Due to the characteristics of the deep neural networks' architecture, two-stage networks are superior compared to one-stage networks in terms of detection accuracy, while one-stage networks are much faster than two-stage networks in terms of detection speed. The real-time operation, limitations, and application scenarios of some representative object detection networks are summarized in Table 1.  
Figure 1: Insulator in transmission lines and missing fault of an insulator. With the continuous development of various hardware and parallel computing technologies, compared with the traditional image processing methods, the overall performance of image processing based on deep learning methods has greatly improved [31]. The application based on deep learning methods on insulator detection has become an important research issue for transmission lines' automatic inspection. Naturally, the existing one-stage networks can be adapted to insulators and their fault detection by a transfer learning strategy. [PERSON] et al. [32] proposed a new method based on multi-level perception for the detection of insulator faults. The detection model with a multi-level perception architecture was trained by an SSD algorithm, and then the well-trained deep learning model was utilized to detect insulator faults automatically. Experimental results demonstrate that the precision and recall of insulator fault detection under diverse background could reach 91.23% and 93.69%, which were obviously superior to those of the traditional image processing algorithms. In the work of [33], a novel intelligent monitoring system based on UAV and a deep learning model was proposed for transmission line inspection. Firstly, to solve the problem of lacking training samples, four component fault datasets were created for the training model. Then, multiple data augmentation techniques were adopted to deal with class imbalance. Finally, a deep learning model based on SSD was proposed for detecting small components and their faults. Experimental results reveal that the common component faults can be detected quickly and accurately by the proposed monitoring system. Although the detection speed of the SSD model has been greatly improved compared to the two-stage networks, the running time of SSD model is not as good as YOLO models [34]. In particular, YOLO-v2 and YOLO-v3 have a good effect on detection speed and accuracy, and have been widely used in the object detection field. In [35], a real-time detection model based on deep neural networks was proposed for the automatic detection of insulators. Firstly, aerial images captured by UAV were collected to create a dataset for training. Secondly, to avoid overfitting, data augmentation techniques were applied to enlarge the training set size. Finally, YOLO-v2 was utilized for insulator detection training and testing in aerial images. It is demonstrated from the experimental results that YOLO-v2 could detect insulators accurately and met the requirements for real-time application. [PERSON] et al. [36] proposed a deep learning method for insulator bunch-drop detection. YOLO-v3 model was adopted to train an insulator dataset for insulator localization and fault detection. The experimental results show that the YOLO-v3 model could effectively localize insulators and detect their faults, and the speed of insulator detection could reach 45 frames per second. In the work of [37], a cascaded deep learning model was proposed to detect insulators and their missing faults. Firstly, an improved YOLO-v3 model based on the backbone network of Resnet 50 was employed to locate insulators. Then, the regions of located insulators were regarded as regions of interest (RoI) for missing fault detection.