Figure 1 in [38] shows that their proposed method has the potential to detect insulator multi-missing-fault. However, since there is currently no publicly-available dataset for insulator missing faults detection, their dataset contains only 10 images with insulator missing faults. Although the machine learning-based methods have increased the accuracy of insulator location and faults detection, all of them have a common limitation in that they are time-consuming as they have to adopt the slide-window strategy to check the whole aerial image. In the past five years, object detection has achieved great breakthroughs with the development of hardware equipment and deep learning theory. Many representative deep convolutional networks such as RCNN [39], Fast-RCNN [40], Faster-RCNN [41], YOLOv1 [42], YOLOv2 [43], YOLOv3 [44], and their variants [45] are proposed and validated on public datasets [46]. These methods depict objects by learning high-dimensional semantic features. Specifically, SPP-Net, RCNN, Fast-RCNN, and Faster-RCNN belong to two-stage networks while YOLOv1, YOLOv2, and YOLOv3 are one-stage networks. The two-stage methods have two common shortcomings: that they are time-consuming and hard to train. On the contrary, the one-stage methods can run in real-time at a moderate expense of accuracy compared with the two-stage methods [47; 48; 49]. Therefore, one-stage methods have higher feasibility for deployment on embedded devices. Motivated by these pioneering researches, it is worth investigating how to use deep learning models to locate insulators and detect faults in aerial images [1]. Although there are few related works, a summary of literatures are given and analyzed as follows: In the work of [40; 41; 48; 49], Fast-RCNN and Faster-RCNN are adopted to locate the insulators. However, the training process of Fast-RCNN and Faster-RCNN is complicated and difficult to deploy. Moreover, they cannot locate insulators in aerial images in real-time. In [50], Faster-RCNN uses rectangle bounding boxes to label the insulator positions in the aerial image. After that, U-net is developed to segment the fault contour in the rectangle bounding boxes. The performance of this cascade framework will degrade in cases in which insulators are serious overlapping. Moreover, there is no public dataset for insulator fault detection. Accordingly, it is hard to train an end-to-end network with good performance for insulator faults detection. To address this challenge, [PERSON] et al. [51] segment the insulator string that contains insulator fault from an aerial image. Subsequently, they paste the segmented insulator string on another aerial image that only contains background to augment their insulator fault dataset. However, the insulator fault in a simulated aerial image is similar to that of the original aerial image: this disadvantage will affect the experimental results, which in turn affect the generalization ability of their proposed network. In general, most of the man-made feature-based methods and machine learning-based methods are quite sensitive to complex background interference. Moreover, their performances are usually suppressed by the filming angle and distance. Furthermore, most of these methods are time-consuming and far from a real-time application. In respect of the existing deep learning methods, since there are no insulator aerial image datasets available from public resources, such methods have not been significantly developed at present. Most importantly, regardless of the existing man-made feature-based methods, machine learning methods, or the deep learning methods, they have no systematic analysis and solve the problem of the insulator multi-fault detection. Therefore, it is meaningful to propose a method that can solve the problems in the existing methods. In this paper, we propose a novel two-step method for insulator faults detection that is based on the CNN feature of UAV aerial images while considering the unique color and area feature of the insulator faults.