# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Ablation Experiment  
In order to verify the effectiveness of the CBAM module for the detection of small target insulators, ablation experiments are also conducted in this paper. The experimental results are shown in Table 2 and Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Overall/\%** & **Insulator/\%** & **Defect/\%** & **Fading/\%** & **Parameters/Mb** & **GFLOPs/G** \\ \hline Without CBAM & 93.8 & 96.9 & 99.1 & 84.9 & 3,940,765 & 7.1 \\ \hline With CBAM & 94.8 & 98.9 & 96.9 & 88.9 & 3,982,355 & 7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of detection accuracy and network complexity before and after the introduction of CBAM.  
As can be seen from Table 2, the introduction of the CBAM module makes the number of parameters of the network rise slightly, but the impact on the computational volume is negligible. The data in the table show that, in the network's added CBAM module, the detection accuracy of all types of labels have risen; particularly, the color-changing insulators show the most obvious improvement at 4%. Our analysis shows that the reason for this may be that the CBAM module can enhance the model in terms of the color of the key features of attention, thus improving the ability of the model to distinguish the color change.  
As can be seen from Figure 7, the overall detection performance of the network has been further improved after the introduction of the CBAM module. By comparing Figure 7a,b, it can be seen that after adding CBAM, the detection accuracy of large insulators has increased from 0.82 to 0.90, and the detection accuracy of discoloration has also increased by 0.05; the detection accuracy of small insulators has also increased from 0.72 to 0.78; by comparing Figure 7c,d, it can be seen that under a relatively simple background environment, adding CBAM does not show much improvement, and the detection accuracy of insulators and defects has only increased by 0.01. Therefore, the introduction of the CBAM module can maintain good overall detection performance while ensuring the network is lightweight and improve the detection performance of small target insulators. In particular, the detection improvement effect is obvious for insulators that account for  
Figure 7: Comparison before and after the addition of the CBAM module: (**a**) Image 1 without CBAM; (**b**) Image 1 with CBAM; (**c**) Image 2 without CBAM; (**d**) Image 2 with CBAM.  
a small proportion of the image background, individual insulators with defects such as missed detection and discoloration, and small targets.  
A comprehensive analysis of the results in Table 2 and Figure 7 shows that the CBAM module significantly improves the accuracy of insulator defect detection while ensuring the network remains lightweight; this not only improves the detection performance of the network but also ensures its high efficiency and real-time performance, making it suitable for practical application scenarios such as UAV inspection and providing a more effective and reliable solution for the intelligent detection of insulator defects in the inspection of power transmission lines.