## 3 Experimental Results and Discussion
### Analysis of the Proposed Insulator Multi-Fault Detection Method
In each row, the first column to the third column depicts the performance of the proposed method, method [20], and method [11], respectively. Figure 9: Experimental results in aerial images with insulator one fault. In each row, the first column to the third column depicts the performance of the proposed method, method [20], and method [11], respectively. To further quantitatively evaluate the performances of the above methods, we test them on two sub-datasets in which the one contains only insulator one fault images while another one contains only insulator multi-fault images. Specifically, each sub-dataset contains 60 samples, and the experimental results are shown in Figure 10(a),b. Based on the observation of Figure 10(a), the proposed method achieves an precision rate of 94.2%, which is much higher than those of method [11] (precision rate: 65%) and method [20] (precision rate: 50%). When considering the multi-fault detection results, the proposed method, method [11], and method [20] achieve precision rates of 98.3%, 88.3%, and 80.8%, respectively. In addition, the average running times of the three methods are also analyzed through the detection of all the 120 aerial insulator faults images, as shown in Table 6. The proposed method takes less running time than the two compared methods. In general, it can be concluded that whether in the insulator one fault detection or the insulator multi-fault detection, the proposed method achieved higher precision rates and lower running time compared with method [11] and method [20]. Most importantly, the proposed method not only achieves high precision rates, but also maintains satisfactory recall rates. Figure 10: The detection performances of the proposed method and two compared methods on 60 insulator one fault images and 60 multi-fault images. The proposed method not only achieves high precision rates, but also maintains satisfactory recall rates. To validate the robustness of the proposed method in different aerial scenes, 90 images are selected and then divided into three sub-datasets: (A) Different backgrounds, (B) different filming angles, and (C) different filming distances. Specifically, each category contains 30 images, half of which contain the insulator one fault, and the other half of which contain the insulator multi-fault. Then, both the proposed method and the two compared methods are tested on each sub-dataset. The results are shown in Table 7. Based on the observation of Table 7, it is observed that the proposed method achieves better results on the three sub-datasets than the Method [11; 20]. Based on the observations of the above experimental results, it can be concluded that the proposed method is more effective and efficient than the two compared methods. The possible reasons for this good performance are given as follows. First, a large number of insulator aerial images are collected to create an unprecedented dataset 'InST_detection' for the proposed network training. Second, the proposed network detected the accurate insulator positions and then removed the complex background interference, which potentially improved the accuracy of the subsequent insulator faults detection. Finally, we systematically analyzed the features of the insulator faults and then propose an effective method to filter the interference contours, which further increased the accuracy of insulator faults detection. Figure 12 shows more detection results of the proposed method in different aerial scenes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Running Times (s/per Image)** \\ \hline Method [11] & 0.677 \\ Method [20] & 0.525 \\ The proposed method & 0.127 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The running times of the different methods. Figure 12: Insulator faults detection results under different aerial scenes. The pink bounding boxes give the insulators positions and the green bounding boxes indicate the detected insulator faults positions.