# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
## 4 GIS Partial Discharge Defect Detection Algorithm
### Comparison of Detection Methods  
GIS partial discharge defect detection models based on SSD, Faster-RCNN, YOLOv3, and YOLOv4 algorithms are established with the same training set and test set ratios to compare the metrics of different detection algorithms with the YOLOv5 model. The number of samples for the four types of partial discharge defects in the test set is 3435, and the number of samples in the test set is 425. The same validation set is used to verify the testing effect of different algorithms, and the detection efficiency and detection speed of each algorithm are judged by comparing the AP value, mAP value, and FPS of each type of algorithm. The detection results of different algorithms are shown in Table 5.  
By comparing the GIS partial discharge defect detection models under these five different algorithms, it can be seen that the YOLOv5 algorithm has the best detection results, both in terms of detection effectiveness and detection efficiency. Although the mAP value of SSD is comparable to YOLOv5 algorithm, and the FPS value is higher than YOLOv5 algorithm, the AP values of detecting the three defects of corona discharge, insulation discharge, and suspension discharge are far from acute for YOLOv5 algorithm. The AP value of YOLOv3 algorithm for corona discharge defect detection is not as good as YOLOv5 algorithm, and the FPS is also lower than YOLOv5. The YOLOv5 algorithm follows the optimized network structure of CSPNet and PANet for YOLOv4 network backbone feature extraction, and adds the Focus network structure to optimize the feature extraction algorithm, which not only improves the network computation speed but also improves the network computation accuracy. The YOLOv5 network is superior to the existing mainstream target detection algorithms in terms of both detection effect and detection speed. The innovation of the GIS partial discharge defect detection model proposed in this paper is that the defect detection does not only tend to be classified, but can be monitored in real time during the practical application. In addition, when one or more types of faults occur at the same time, or when multiple faults occur within a short period of time, defect detection using the GIS partial discharge defect detection model proposed in this paper can be effectively identified and early warnings made in a timely manner to prevent serious  
Figure 11: Result of PD defects detection. (**a**) metal tip defects, (**b**) floating electrode defects, (**c**) insulating void defects, (**d**) free metal particle defects. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multirow{3}{*}{**Resolution**} & \multicolumn{6}{c}{**AP/\%**} \\ \cline{3-8}  & & **Corona** & **Insulation** & **Electode** & **Free Particle** & **mAP/\%** & **FPS** \\  & & **Discharge** & **Discharge** & **Discharge** & **Discharge** & **Discharge** & **Discharge** \\ \hline SSD & 300 \(\times\) 300 & 94.57 & 96.74 & 92.49 & 97.31 & 95.28 & 29.34 \\ Faster-RCNN & 600 \(\times\) 600 & 97.20 & 98.71 & 82.81 & 95.73 & 93.16 & 18.42 \\ YOLOv3 & 416 \(\times\) 416 & 93.57 & 96.06 & 95.99 & 96.17 & 95.45 & 27.39 \\ YOLOv4 & 416 \(\times\) 416 & 92.88 & 70.51 & 90.17 & 68.21 & 80.44 & 27.45 \\ YOLOv5 & 640 \(\times\) 640 & 96.74 & 97.01 & 93.76 & 96.06 & 95.89 & 28.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection results of different algorithms. faults in GIS equipment and affect equipment operation. Multi-defect detection effect is shown in Figure 12. The situation when multiple types of defects appear one after another in a short period is represented by Figure 12a.