# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Results  
Figure 8 shows the test results of the last epoch of the training process. Figure 8 shows the labeled visualization result, where all the contents in the labeled file are fully presented on the image. On the other hand, Figure 8 shows the test results of the network on the same batch of images, again presented in the form of boxes on the image. As can be seen from the figure, the model has achieved a high accuracy of more than 0.9 in detecting insulator defects, and the average detection accuracy for large insulators is 0.94. Although the detection accuracy of small insulators is only 0.76, for small insulators with complex backgrounds, such as the picture in the first row and third column of Figure 8, all insulators can be accurately detected. This shows that the improved model can not only accurately identify and locate insulators of different sizes but also efficiently detect insulator defects. This high-precision detection capability is very important for practical applications and can effectively improve the efficiency and reliability of transmission line inspection.  
Figure 8: _Cont._  
To further assess the model's robustness, it was tested on samples both before and after the addition of noise. Figure 9 presents a comparative analysis of the model's performance in detecting insulator defects under these two conditions.  
To further evaluate the robustness of the model, the samples were tested before and after adding noise. Figure 9 shows a comparative analysis of the performance of the model in detecting insulator defects under these two conditions. As can be seen from the figure, when no noise is added, the accuracy of the model for the overall insulator in the same picture is 0.84, and the accuracy for the defective insulator is 0.89; when noise is added to the picture, the accuracy of the model for the defective insulator decreases by 0.02, but the accuracy for the overall insulator increases by 0.05. This observation shows that the model maintains a high level of detection accuracy and completeness even when the accuracy  
Figure 8: A detailed diagram of the test’s visualization results: (**a**) label chart; (**b**) prediction chart.  
Figure 9: Detection result: (**a**) before adding noise; (**b**) after adding noise.  
of defective insulator detection is reduced due to noise interference. Despite the adverse effects of noise, the model can still maintain comprehensive defect identification, which highlights its robustness and reliability in noisy environments and enhances its potential in practical applications where such challenges are prevalent.  
It can also be seen from the comparison graph that the addition of noise has little effect on the overall detection of insulators. The model is still able to accurately identify and locate insulators in the presence of noise. This indicates that the improved, optimized model performs well against noise interference, with strong robustness and stability.  
The enhancement in anti-noise performance is particularly critical for practical applications, especially in the context of transmission line inspections where image data are often subject to interference from various external factors, such as adverse weather conditions and variations in the performance of filming equipment. The model proposed in this study demonstrates the capability to maintain high accuracy and robust detection integrity even in the presence of noise, thereby significantly enhancing the practicality and reliability of the system in complex, real-world environments. This improvement underscores the model's suitability for deployment in challenging operational scenarios where consistent performance is essential.