## 4 Experimental Results and Discussion
### Experimental Results and Comparative Evaluation
#### 4.3.2 Comparison of Different Testing Methods  
In order to rigorously assess the performance enhancements of the refined YOLOv8_Adv model, a thorough comparative evaluation is performed, benchmarking it against five established baseline models YOLOv8n [37], YOLOv8_Faster Net [38], YOLOv8_Gsconv [39], YOLOv8_fastC2f [40], and YOLOv8_Biformer [41]. Figure 11 presents a comparative analysis of the P-R curves for the improved YOLOv8_Adv model against various baseline models. As illustrated, the YOLOv8_Adv model exhibits superior performance on the P-R curve, with both precision and recall approaching the ideal value of 1. This indicates that the model effectively identifies positive samples in detection tasks while maintaining a relatively low false positive rate, achieving an improvement of approximately 4.1% compared to the original YOLOv8n. In contrast, the YOLOv8_FasterNet and YOLOv8_Biformer models demonstrate satisfactory precision within high recall intervals, yet their overall performance does not match that of the YOLOv8_Adv. Furthermore, other models, including the YOLOv8n, YOLOv8_Gsconv, and YOLOv8_fastc2f, exhibit a significant decline in precision at elevated recall levels, suggesting that these models may compromise accuracy while enhancing recall, potentially resulting in a higher false positive rate. Figure 12 illustrates the comparison of [EMAIL_ADDRESS] curves between the improved YOLOv8_Adv and the other baseline models. The results show that after training iterations exceed 65 epochs, the average precision of the YOLOv8_Adv significantly outperforms the baseline models. After 500 iterations, the [EMAIL_ADDRESS] of YOLOv8_Adv increased from 94.9% to 98.7%, marking an improvement of 4.0%. Figure 11: Comparative analysis of precision–recall (P–R) curves for different models. It is evident that YOLOv8_Adv achieves the highest [EMAIL_ADDRESS] towards the end of the training phase, maintaining stability thereafter. This indicates that the model possesses superior learning and generalization capabilities. While other models also stabilize by the end of training, they do not reach the same level as YOLOv8_Adv during the same period (e.g., between epochs 20 and 80). Furthermore, the convergence speed highlights a key difference: YOLOv8_Adv experiences a rapid increase in [EMAIL_ADDRESS] in the early stages of training, indicating fast convergence and demonstrating its ability to effectively extract features. Table 1 presents a comparison of data values between the improved YOLOv8_Adv model and the baseline models. The comparison is conducted using a training dataset comprised exclusively of augmented infrared images of substation equipment. The augmentation process significantly improves the model's robustness and performance by introducing a wide variety of scenarios and environmental conditions. The data presented in Table 1 indicates that under the condition of 640 \(\times\) 640 pixel images, the YOLOv8_Adv model demonstrates superior overall performance, with the highest \(P\) and \([EMAIL_ADDRESS]\) among all compared models, reaching 97.3% and 98.7%, respectively. Moreover, despite exhibiting a marginal reduction of only 0.1% in recall (R) compared to the top performing YOLOv8_Biformer model, the YOLOv8_Adv model achieves a significantly lower GFLOPs value of just 10.1, underscoring its superior efficiency and precision in object detection tasks. The YOLOv8_Adv demonstrates improvements of 5%, 5.2%, and 3.9% in P, R, and [EMAIL_ADDRESS], respectively, when compared to the baseline YOLOv8n model. Simultaneously, its GFLOPs value also decreases by 2.3, further demonstrating its significant performance enhancement relative to the baseline model. It is worth noting that when we focus solely on the GFLOPs value, YOLOv8_FasterNet offers relatively good