## 3 Experiments Results and Discussion
### Quantitative and Qualitative Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Networks** & **AP** & **Precision** & **Recall** & **F1 Score** & **Running Time (s)** \\ \hline YOLO-v3 & 93.31\% & 94\% & 94\% & 94\% & 0.01 \\ Literature [38] & 95.07\% & 97\% & 95\% & 96\% & 0.011 \\ YOLO-v4 & 96.38\% & 98\% & 95\% & 97\% & 0.01 \\ CSPD-YOLO & 98.18\% & 99\% & 98\% & 99\% & 0.011 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The experimental effects of different networks. Moreover, the aerial images used for the dataset 'InSF-detection' include diverse background interference in order to evaluate the performance of insulator fault detection. The experimental results with different scenes analyzed by the CSPD-YOLO model are shown in Figure 9a-e; in each figure, the pink rectangular box is used to locate the position of the insulator faults. Specifically, Figure 9a-c present the experimental results with one-fault or multiple fault in scenes of rivers, vegetation, and power towers, respectively. As the backgrounds of rivers and vegetation are simple, the insulator faults in Figure 9a,b were easily detected by the CSPD-YOLO model. Although the background of power towers is complex in Figure 9c, all the faults were accurately detected by the proposed CSPD-YOLO model. Figure 9d demonstrates the experimental scene with occlusion of insulator strings, and Figure 9e exhibits the experimental scene with the insulator's color being similar to the background. It is difficult to achieve effective insulator fault detection using traditional image processing methods in these cases; however, as can be seen from Figure 9d,e, all the insulator faults were accurately detected by the CSPD-YOLO model. Therefore, the experimental results from Figure 9 reveal that the proposed CSPD-YOLO model can obtain good efficacy in the detection of insulator faults in aerial images with the interference of complex backgrounds. Figure 8: The P–R curves of the proposed network and the compared networks. Figure 9: _Cont._  
To further verify the effectiveness and robustness of the proposed CSPD-YOLO model, several typical aerial images were selected to exhibit the visualization performance of the proposed CSPD-YOLO model. Figure 9: Experimental results with different scenes conducted by the CSPD-YOLO model. YOLO-v3, the literature model [38], YOLO-v4, and the CSPD-YOLO model; as shown in Figures 10-12, the positions of insulator faults were located by pink rectangular boxes. Specifically, the experimental results with a river background are shown in Figure 10; since the insulator faults are relatively small, only two insulator faults were detected by YOLO-v3 (Figure 10a), the model in the literature [38] (Figure 10b), and YOLO-v4 (Figure 10c), while all of the insulator faults were correctly detected by the CSPD-YOLO model (Figure 10d). Figure 11 demonstrates the experimental results with the background of trees; although the background is complex, all of the insulator faults were detected by the CSPD-YOLO model (Figure 11d), while two insulator faults were detected by YOLO-v3 (Figure 11a) and the literature model [38] (Figure 11b), and three insulator faults were detected by YOLO-v4 (Figure 11c). The experimental results with a power tower background are shown in Figure 12; due to the color of background being similar to that of the insulator and the power tower background being complex, two insulator faults were detected by YOLO-v3 (Figure 12a), three insulator faults were detected by the literature model [38] (Figure 12b) and YOLO-v4 (Figure 12c), while all of the insulator faults were detected by the CSPD-YOLO model (Figure 12d). Consequently, compared with YOLO-v3, the literature model [38], and YOLO-v4, the proposed CSPD-YOLO model performs better for insulator fault detection in aerial images with complex backgrounds. It can be concluded that the proposed CSPD-YOLO model has advantages in insulator fault detection in inspection images with complex backgrounds and small objects.