# Transmission Lines Insulator State Detection Method Based on Deep Learning
## 3 Results  
### Comparison  
To validate the superiority of the proposed method in detection performance compared to other methods and to comprehensively assess the performance of this model, various comparative experiments were conducted using multiple methods. Five object detection methods were compared in this experiment, including SSD, YOLOR [27], YOLOv5, YOLOv10, and the original YOLOv7. All experiments were conducted on the same experimental platform as the method proposed in this paper, using a consistent dataset. The performance metrics of the six comparative methods are presented in Table 2. Among them, the SSD employs VGG16 as its backbone network. Despite having the smallest parameter size among the six methods, its detection accuracy is comparatively lower due to its weaker feature extraction capability. YOLOv5 adopts CSPNet [28] as its backbone network. The YOLOR optimizes its detection accuracy by encoding implicit and explicit knowledge. YOLOv10 [29] optimizes both efficiency and accuracy perspectives, which reduces the computational overhead and enhances the capability. With regards to [EMAIL_ADDRESS] on this dataset, the other five methods, apart from the proposed improved method, all achieve below 90%. In contrast, the proposed method achieves an [EMAIL_ADDRESS] of 97.1%, demonstrating superior accuracy, recall, and F1 score compared to other methods. This confirms the effectiveness and superiority of the proposed method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **P (\%)** & **R (\%)** & **[EMAIL_ADDRESS] (\%)** & **F1** & **Params (M)** \\ \hline SSD (640) & 72.7 & 52.3 & 70.2 & 0.46 & 26.1 \\ YOLOR (640) & 67.1 & 84.6 & 86.5 & 0.88 & 36.8 \\ YOLOv5 (640) & 87.8 & 85.9 & 85.9 & 0.81 & 45.4 \\ YOLOv7 (640) & 91.1 & 83.4 & 88.8 & 0.87 & 37.2 \\ YOLOv10 (640) & 84.6 & 72.7 & 78.9 & 0.78 & 2.7 \\ Ours (640) & 98.8 & 94.0 & 97.1 & 0.96 & 38.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different detection methods. Figure 8: Detection results of improved YOLOv7. The changing trend of loss during iteration is illustrated in Figure 9, which depicts the loss function curves for the validation processes of the six methods over 100 iterations. As observed from Figure 9, except for the SSD, the losses of the remaining five methods decrease rapidly in the early iterations, gradually slowing down and eventually converging to a specific value after a certain number of iterations. Utilizing boundary box regression loss based on IOU measurement, the losses of these five methods eventually stabilize below 0.05. Conversely, the SSD employs boundary box regression loss based on the L1 norm, resulting in significantly higher losses and a slower decline in loss during the optimization process. The loss curve of the proposed method decreases from 0.21 to around 0.02, exhibiting the smallest convergence value and a smoother change in loss compared to other methods. This suggests that the proposed method updates parameters more accurately along the steepest gradient descent path, highlighting the advantages of the improved YOLOv7 proposed in this paper. To intuitively demonstrate the detection performance of the proposed method for the insulator state, a comparative analysis was conducted between the proposed method and SSD, YOLOR, YOLOv5, YOLOv10, and the original YOLOv7 on the test set. A class confidence threshold of 0.5 and an intersection over union (IoU) threshold of 0.75 were set. The detection results are depicted in Figure 10. Detected targets are marked with bounding boxes of different colors to represent different categories, with class labels and class confidence provided in the upper left corner of the bounding boxes. Among these five detection results, SSD performs the worst, exhibiting missed detections for both small and large targets and poor performance in identifying occluded targets. YOLOR, YOLOv5, YOLOv10, and the original YOLOv7 all exhibit varying degrees of false positives and false negatives.