# Transmission Lines Insulator State Detection Method Based on Deep Learning
## 3 Results
### Comparison
In contrast, the proposed method shows no instances of false positives or false negatives in these five images, with most detected target class confidences higher than those of other methods. Through comparative experimental analysis, it is evident that the proposed method for detecting insulator state in transmission line based on BRA-CBS and content-aware up-sampling performs better in detection performance, producing results more in line with engineering reality and demonstrating good generalization and robustness. Figure 9: Comparison based on loss curve of validation. Figure 10: _Cont._  
Figure 10: Detection results of comparison experiment. (**a**) SSD; (**b**) YOLOv10; (**c**) YOLOv7; (**d**) YOLOR; (**e**) YOLOv5; (**f**) Ours. Figure 12: Precision–recall curves of ablation experiments. (**a**) Group A; (**b**) Group B; (**c**) Group C; (**d**) Group D; (**e**) Group E; (**f**) Group F.  
Group A utilized the original YOLOv7 network, trained on raw data of size 640 \(\times\) 640 pixels without data augmentation. Groups B, C, and D, built upon Group A, employed mosaic data augmentation as input. Additionally, they replaced the bounding box regression loss function CIOU with WIOU, replaced nearest neighbor up-sampling with content-aware up-sampling operator, and replaced CBS with the fusion convolution module BRA-CBS with a bi-level routing attention mechanism, respectively. Group E is the proposed method but without data augmentation. Finally, Group F represents the proposed method. From Tables 3 and 4, as well as Figures 11 and 12, it can be observed that under the same training epochs, data augmentation significantly enhances the method's detection accuracy. Moreover, the [EMAIL_ADDRESS] of the proposed method shows smoother variations and converges relatively faster. Given that the samples of insulator strings and flashover constitute the majority, and the outlier assessment method of WIOU enhances learning with large sample sizes, Group B, which introduces WIOU, achieves the highest recognition accuracies ([EMAIL_ADDRESS]) for insulator strings and flashover, reaching 99.7% and 98.8%, respectively. Group C, utilizing the content-aware up-sampling operator, demonstrates favorable performance in the recognition accuracy of the self-explosion and the category recognition accuracy and only slightly increases the parameter size by 0.5% compared to the original YOLOv7. Group D, employing the BRA-CBS module, exhibits outstanding recall rates. Comparatively, the proposed method in Group F increases the parameter size by 4% compared to the original YOLOv7. However, its precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 metrics improve by 7.7%, 10.6%, 8.3%, and 19.7%, respectively, over the original YOLOv7. It achieves recognition accuracies ([EMAIL_ADDRESS]) of 93.5% and 98.6% for self-explosion and breakage, which have fewer samples. Through the analysis of the ablation experiments, it is evident that the improved YOLOv7 proposed in this paper not only introduces fewer parameters but also significantly outperforms the original YOLOv7 in terms of detection accuracy, demonstrating the superiority and effectiveness of this method.