# High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5
## 4 Discussion
### Ablation Experiment  
In order to verify the effectiveness of the improved algorithm proposed in this paper, different experimental groups are set up, and different experimental modules are replaced and added for experimental verification. The experimental results are shown in Table 2 below:  
Based on the original network, the Ghost lightweight module is set in turn, the network detection layer is increased, and the CRF receptive field module is designed. Comparing the modules added in this paper with the original network, comparing method 1 with method 2, the accuracy of model detection decreases by 0.5% on average with the Ghost lightweight module, but improves the speed of model detection and reduces the amount of network computation with the Ghost module. Comparing method 3 with method 2, the addition of detection layers reduces the recall for the category D-insulator but increases the accuracy due to the increased detection scale for the small area target of the defective insulator. The detection layer set up to constitute the new model architecture improves accuracy for both categories of detection, and the overall performance of the network improves. Comparing method 4 with method 3, replacing the SPP module with the CRF receptive field module improves the detection accuracy of the model by 1.9%. By analyzing the experimental results, we can see the effectiveness of the module designed in this paper.  
As shown in Figure 6, the light blue curve is the P-R curve of the N-insulator category, the orange curve is the P-R curve of the D-insulator category, and the dark blue curve is the P-R curve of the overall average accuracy of the network. In the P-R graph, the abscissa is the recall rate, and the ordinate is the precision rate. The larger the area enclosed by the curve and the abscissa and ordinate axes, the closer the curve is to the upper right corner, and the better the network performance. The four experimental groups combined with the ablation experiment produced their corresponding P-R curves, arranged in order, as shown in Figure 6. By comparing the four graphs, it can be seen that the area enclosed by the curve and the horizontal and vertical axes in image (d) is larger, the accuracy and recall rate is higher, and the network model performance is better than the first four.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Ghost** & **Incease of** & **CRF Receptive** & \multicolumn{2}{c}{**N-Insulator**} & \multicolumn{2}{c}{**D-Insulator**} & \multirow{2}{*}{**[EMAIL_ADDRESS]**} \\  & **Module** & **Detection Layer** & & **Field Module** & **P** & **R** & **P** & **R** \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & 0.845 & 0.805 & 0.933 & 1 & 0.901 \\
2 & \(\surd\) & \(\times\) & \(\times\) & 0.814 & 0.771 & 0.976 & 1 & 0.896 \\
3 & \(\surd\) & \(\surd\) & \(\times\) & 0.909 & 0.879 & 0.99 & 0.979 & 0.955 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & 0.915 & 0.908 & 0.988 & 1 & 0.974 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance index comparison of ablation experiment.