# High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5
## 4 Discussion
### Contrast Experiment  
The algorithm in this paper is compared with Faster R-CNN [33], which is the typical representative of the two-stage detection algorithm in the current target detection algorithm, CenterNet [34] based on anchor-free, and the original YOLOv5 algorithm.  
As can be seen from Table 3, Faster R-CNN has a higher recall rate for normal insulators, but a lower detection accuracy and slower detection speed for defective insulators in small areas. The performance of CenterNet is significantly lower than that of other algorithms, and its detection accuracy of defective insulators is high. However, CenterNet cannot detect the number of defective insulator images in the dataset well. In the above network model, the detection of the category D-insulator is poor. Compared with the two-stage detection model, the detection accuracy is improved by 14.2%, and the detection speed is also improved by 50 FPS. Compared with the detection model without an anchor frame, the detection accuracy is improved by 19.8%, and the detection speed is also improved. Compared with the original model, the detection accuracy is improved by 7.3%, and the detection speed is also improved by 9 FPS. At the same time, the improved network has the best detection accuracy for normal insulators, and the network running speed is also improved.  
The image in Figure 7 is the detection result map under different network models, where (a), (b) and (c) columns are three different insulator images to be detected. The detection results of the four network models are in turn. Through the picture, it is more  
Figure 6: P-R curve. (**a**) P-R curve of method 1 in ablation experiment; (**b**) P-R curve of method 2 in ablation experiment; (**c**) P-R curve of method 3 in ablation experiment; (**d**) P-R curve of method 4 in ablation experiment.  
clear and more intuitive to see that Faster-RCNN in column (a) misdetects the defective insulator as a normal insulator. The CenterNet network model misdetects the enclosure in the background as a normal insulator and the CenterNet in column (b) does not detect the defective insulator, resulting in a missed detection. The original YOLOv5 model also shows false detection of the wall in the defective insulators in column (a) and false detection of defective insulators. For defective insulators listed in (a), the method presented in this paper has more advantages. For normal insulators listed in (c) for detection, it can be seen that the results of the four models are better for the detection of normal insulators. Combined with network detection indicators and intuitive picture results, it can be seen that this method has higher detection accuracy and faster detection speed. From the detection accuracy and speed, this algorithm has more advantages.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Module**} & \multicolumn{2}{c}{**N-Insulator**} & \multicolumn{2}{c}{**D-Insulator**} & \multirow{2}{*}{**[EMAIL_ADDRESS]**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{5-6}  & **P** & **R** & **P** & **R** \\ \hline Faster-RCNN & 0.7541 & 0.9459 & 0.4375 & 0.913 & 0.832 & 12 \\ CenterNet & 0.825 & 0.5766 & 1 & 0.1304 & 0.776 & 56 \\ YOLOv5 & 0.845 & 0.805 & 0.933 & 1 & 0.901 & 53 \\ Ours & 0.915 & 0.908 & 0.988 & 1 & 0.974 & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different models.  
Figure 7: Detection results of different network models. (**a**) Figure (A) shows the detection results under different network models; (**b**) Figure (B) shows the detection results under different network models; (**c**) Figure (C) shows the detection results under different network models.