# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 1 Introduction  
The insulator is an important piece of equipment that provides mechanical support and electrical insulation in the catenary [1, 2], and its status directly affects whether the railway power supply system can work normally. Typical defects of catenary insulators include surface contamination caused by the natural environment, aging caused by long-term electromechanical loads, flashover caused by increased humidity and icing, cracks caused by temperature changes, and damage caused by external forces such as hail and debris impacts [3]. Among them, contamination, flashover, and natural aging of catenary insulators can be avoided by regular cleaning and replacement, while the cracking and damage of insulators will not only affect the insulation characteristics of the power line, but also affect the service life of the entire traction network, undermine the safety and stability of the railway power supply system, and even interrupt the operation of the railway [4]. The traditional insulator damage-detection method mainly relies on manual inspection with large workloads and low detection efficiency [5]; this method cannot find and deal with the insulator fault in time, affecting the safety of the whole traction power supply system [6]. With the rapid development of high-speed railway monitoring and fault detection technology, such as the monitoring and detection of the insulation components of the catenary in 6C systems applied in China railway since 2012, intelligent detection methods of railway catenary insulator damage have been used [7]. In the past, various intelligent detection methods based on images have been studied. The proposed detection methods can be divided into feature extraction methods [8, 9, 10], deep learning methods [6, 11, 12, 13], and deep learning and feature extraction combined methods [14, 15, 16]. For example, a Harris corner matching and spectral clustering method isproposed to achieve fault detection of catenary insulators [9], but this method is easily affected by image brightness and complex environmental background. A catenary insulator fault detection algorithm is proposed based on YOLOv5 [12], but a large number of damaged insulator samples are needed to improve the accuracy of the algorithm. Moreover, as a small target in the image, it is difficult to judge whether the insulator is damaged with high accuracy [15] proposed to use the EAST model to locate the insulator firstly, then extract the contour of the insulator piece, and use the Hu invariant moment to compare the similarity between the insulator pieces to judge whether it is damaged or not. It is less affected by complex environmental backgrounds and does not need a large number of damaged insulator image samples, but for insulator photos taken from the front, the extracted contours of insulator pieces are thin, and the Hu moment algorithm will produce deviation and misjudgment when calculating the contour features of such cases. Therefore, it still needs to be improved in the description of contour details. To sum up, there are three main problems to be improved and solved in the intelligent detection method of damaged catenary insulators:  
* The traditional non-deep learning feature-extraction algorithm is prone to misjudge under the influence of complex environment and image brightness differences, and its reliability is not strong;
* The insulator damage-detection method completely based on deep learning needs to rely on a large number of damaged insulator samples, which has high requirements for the scope and workload of image acquisition;
* The features extracted by some characteristic moment algorithms based on insulator contours are not detailed enough, and there is still room for improvement in accuracy. Aiming at the above problems, this paper proposes a catenary insulator damage-detection method based on deep learning and Zernike moment algorithms. Firstly, the catenary insulators are identified and positioned by the Mask R-CNN algorithm, and the insulator image is further cropped.