# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 4 Experimental Testing and Analysis
### Damage Detection of Insulators  
After the identification and positioning of the catenary insulators is completed, the insulator area is automatically cropped according to the above-mentioned process, and the preprocessing steps of the insulator small image are completed successively, including bilateral filter filtering, RAISR algorithm to improve resolution, adaptive threshold binarization and insulator single-piece contour extraction, and then calculation of the seventh order Hu moments or 10 th-order Zernike moments of the insulator single-piece contour. The similarity value between different insulator pieces of the same insulator is further calculated according to the characteristic moment, and the similarity standard deviation of each insulator piece is calculated through the similarity value. When the maximum standard deviation exceeds the set threshold of 1.18, the insulator is judged to be damaged. According to above method, 512 insulator images are used for the experimental test, including 29 damaged insulator images and 483 undamaged insulator images. The normal insulator and its contour extraction results are shown in Figure 11a. Considering the amount of calculation and the real-time performance of the algorithm, the 10 th-order Zernike moments are used to calculate the contour similarity value between insulator pieces and the similarity standard deviation of each insulator piece. As is shown in the red part of Figure 12, the maximum similarity standard deviation obtained is 0.5335, which is less than the set threshold of 1.18. Taking the ninth insulator piece from left to right as a reference, the similarity value between its contour and that of other insulator pieces is shown in the blue part of Figure 12.  
Therefore, it can be seen that the contour similarity value between the insulator pieces is small, and the maximum similarity standard deviation is also small, so it can be judged that the insulator is not damaged. Figure 11: Some test images and contour extraction results. (**a**) Normal insulator and its contour extraction results; (**b**) Damaged insulator and its contour extraction results. Figure 12: Similarity standard deviation and similarity value between pieces of normal insulator shown in Figure 11a. The damaged insulator [15] and its contour extraction results are shown in Figure 11b. The 10 th-order Zernike moments are used to calculate the contour similarity value between the insulator pieces and the similarity standard deviation of each insulator piece. As is shown in the red part of Figure 13, the maximum similarity standard deviation is 1.4285, which exceeds the set threshold of 1.18. Taking the second insulator piece from left to right as a reference, the similarity value between its contour and that of other insulator pieces is shown in the blue part of Figure 13. It can be seen that the contour of the second insulator piece and the fifth insulator piece have the highest similarity value (i.e., the least similarity), reaching 1.9299, and the maximum similarity standard deviation is also larger, so it can be judged that the insulator in Figure 11b is damaged. For the insulator with large damage range [15] shown in Figure 14, only 10 insulator pieces can be extracted. The maximum standard deviation of contour similarity value is 0.4397, which is shown in the red part of Figure 15a. Taking the third contour from the left as a reference, the similarity value between its contour and that of other insulator pieces is shown in the blue part of Figure 15a. It can be seen that the similarity standard deviation and the similarity value between the insulator pieces are both small, so it is difficult to judge that the insulator is damaged. At this time, it can be further judged by calculating the distance between the centroids of each insulator piece contour (distance between pieces). Figure 15b shows the distance between pieces of adjacent insulator pieces.