_Applied_  
_sciences_  
_Article_  
**A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission**  
**Lines Inspection**  
**[PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 3, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 4 and [PERSON] 4\(\copyright\)**  
Footnote 1: [[https://github.com](https://github.com)]([https://github.com](https://github.com))  
the influences such as lightning strikes, material aging, and overloading, insulator faults usually occur after a period of running time, as shown in Figure 1. Therefore, effective detection of the insulator faults is an important task for high-voltage transmission line inspection. In traditional manual inspection, people have to walk along paths near high-voltage transmission lines, and then check each insulator status by using various types of instruments, such as audition sensors [7], infrared imagers [8; 9; 10], cameras [11], and ultraviolet imagers [12]. However, the traditional manual method is inefficient and not feasible in practice as high-voltage transmission lines are usually built in complex surroundings containing forests and lakes. Recently, with the development of the unmanned aerial vehicle (UAV) control and image processing technique, insulator status inspection has trended towards the analysis of aerial images captured by UAVs [13]. The existing methods can be generalized into three main categories: (1) Man-made features-based methods, (2) machine learning-based methods, and (3) deep learning-based methods. In the man-made feature-based methods, multiple features including color [11; 14], shape [15; 16; 17], edge [18; 19], gradient [20], texture [21], key-points [22; 23; 24; 25] and their fusions [26; 27] have been explored. Meanwhile, some mathematical models have also been applied, such as the snake model [28], Hough transform [29], Active Contour Model [30], Fuzzy c-means [31], and Receptive field model [32]. In the work of [PERSON] [14], a threshold filtering scheme based on Lab color space is proposed to locate the insulators in aerial images. Then, the coordinates of each insulator are obtained through a developed mathematical model. Finally, the insulator fault is determined by the ratio of the insulator area to its external rectangle area. However, this method can be significantly affected by the complex background contains objects that are similar in color to the insulators. Moreover, its performance will degrade in cases in which insulators are overlapped. To solve this problem, [PERSON] et al. [20] propose a two-step strategy to obtain more accurate insulator locations. First, the insulators are roughly located by a saliency detection method. After that, the insulators are finely segmented from the background through a series of rules. Finally, an adaptive morphology method is proposed to detect the insulator fault. However, this method cannot achieve good performances in complex scenes that contain various types of background interference which are usually more salient than the insulators. Moreover, this method can only detect one fault in an insulator string, and cannot detect multi-fault in an insulator string. In [17; 28], the Otsu algorithm is applied to obtain the insulator regions. Subsequently, the insulator  
Figure 1: Insulator string (Red color) is composed of evenly spaced insulators, and it will have insulator fault (Blue color) after running for a period of time. At present, an important reason for the low effectiveness of insulator detection and insulator fault detection is the complex background interference (Yellow color) in the aerial image. contours are achieved by the wavelet modulus maximum method or snake model. Finally, each insulator contour is fitted to an ellipse by the Hough transform or the least square method, and the insulator fault can be judged by calculating the number of insulators. However, both [17; 28] can only determine whether there are insulator faults in the aerial image but cannot give the positions of the insulator faults.