# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Experimental Result Analysis  
Figure 6 illustrates the curves of precision and recall metrics as they vary with epochs during training. It can be observed from the curves that the network converges rapidly in the early stages of training, with both precision and recall reaching approximately 0.9 after around the 30 th epoch. As training progresses, the two metrics gradually stabilize around the 50 th epoch and eventually settle at a high level above 0.97 by the 200 th epoch. Thisindicates that the model exhibits strong detection performance and generalization ability on this dataset. Moreover, the stability of precision and recall suggests that the model did not experience significant overfitting or underfitting during training, validating the effectiveness and robustness of the designed network. Overall, these results demonstrate the excellent performance of the proposed method on this dataset, as well as the rationality of the network architecture and training strategy. To comprehensively evaluate the performance of the optimized lightweight model, comparative experiments were conducted using the CPLID dataset. It is a large-scale and high-resolution dataset specifically developed for the detection of defects in power line insulators under various real-world conditions, including lighting variations, complex backgrounds, and multiple types of defects such as cracks and damage. Each image in the dataset was meticulously annotated by domain experts to ensure accuracy and reliability, making CPLID a robust benchmark for assessing defect detection performance. The optimized models were trained and tested on the CPLID dataset together with several typical detection frameworks (such as YOLOv3, SSD, and Faster RCNN). The hyperparameter settings of these frameworks were set according to the parameters recommended in their original papers. By training and testing these models on the same dataset, their specific performance data under different evaluation criteria were obtained. All models were run on the same hardware environment, and evaluation indicators such as mAP, precision, and recall were calculated to ensure a fair and consistent comparison. The experimental results, as shown in Table 1, demonstrate significant improvements in the accuracy and efficiency achieved by the optimized lightweight model compared to other models. The evaluation criteria values for the other frameworks were obtained by replicating their experiments using the same CPLID dataset, following the hyperparameters and settings outlined in their respective publications. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Insulator Detection** & **Defect Detection** & **Precision/\%** & **Recall/\%** & **[EMAIL_ADDRESS]\%** & **GFLOPs/G** \\ \hline SSD [23] & 89.3 & 87.2 & 79.6 & 80.7 & 88.3 & 115.7 \\ \hline Fast R-CNN [24] & 87.9 & 63.6 & 64.8 & 85.9 & 75.8 & 130.4 \\ \hline MobileNetv3 [31] & 88.4 & 80.8 & 92.7 & 76.3 & 84.6 & **6.3** \\ \hline YOLOv3 [39] & 91.6 & 82.2 & 90.5 & 83.5 & 87.9 & 154.6 \\ \hline ShuffleNetv2 [40] & 80.2 & 84.4 & 91.5 & 76.5 & 82.3 & 8.0 \\ \hline YOLOv8s [41] & 96.3 & 96.2 & 96.0 & 96.9 & 96.3 & 13.5 \\ \hline Our Improved Method & **98.9** & **97.8** & **97.4** & **98.4** & **98.3** & 7.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between lightweight models and other models. Figure 6: The performance curves during training: (**a**) recall; (**b**) precision. From the table, it can be seen that for the detection of insulators, which involves multi-scale detection tasks with larger insulator targets and smaller defect targets, two-stage object detection algorithms like Fast R-CNN perform poorly. Particularly for detecting smaller defect targets, the detection accuracy is only 63.6%, much lower than that of other single-stage object detection networks. This indicates the rationality of selecting the YOLO network based on a single-stage object detection algorithm for lightweight improvement in this study.