# EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR
## 4 Experiments and Analysis
### Comparative Experimental Results and Analysis
#### 4.4.2 Comparative Experiments  
From Table 2, we can observe that EER-DETR outperforms the original model RT-DETR in multiple key indicators. In terms of Precision, EER-DETR achieved 0.916, while RT-DETR only reached 0.893, indicating that EER-DETR is more accurate in identifying targets and can more effectively reduce false alarms. In terms of Recall, EER-DETR also demonstrated an advantage, reaching 0.872, while RT-DETR was 0.841. This suggests that EER-DETR is more comprehensive in detecting targets and can more effectively reduce missed detections. In terms of mAP50 (mean average precision at 50%) and mAP50-90 (mean average precision at different IoU thresholds), EER-DETR also performed well, reaching 0.905 and 0.458, respectively, while RT-DETR was 0.886 and 0.456, respectively. Compared to the original model, this indicates an improvement of 1.9%. This shows that EER-DETR has superior comprehensive performance across different detection difficulties. Additionally, EER-DETR also demonstrated advantages in terms of parameter quantity and computational complexity. The parameter quantity of EER-DETR is 17,941,933, while that of RT-DETR is 19,875,612. The computational complexity (GFLOPs) of EER-DETR is 48.6, while that of RT-DETR is 56.9. This indicates that EER-DETR maintains high performance while having better computational efficiency and model lightweight characteristics. Figure 7 shows a comparison chart of the detection performance between EER-DETR and RT-DETR. It can be seen that the performance of our model has improved significantly. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **mAP50** & **mAP50-90** \\ \hline Crack & 0.891 & 0.855 & 0.864 & 0.461 \\ Grid & 0.882 & 0.862 & 0.914 & 0.442 \\ Spot & 0.943 & 0.897 & 0.938 & 0.451 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test results for three defect detection types. Figure 6: The feature graphs of different layers in the training process. To verify the stability of the model performance and the reliability of the improvements, five independent experiments were conducted on RT-DETR and EER-DETR, with the same data division and training configuration used in each experiment. The stability of the evaluation metrics was assessed by calculating the mean and standard deviation, and the paired \(t\)-test was used to verify whether the performance difference between the improved model and the original model was statistically significant (significance level \(\alpha=0.05\)). The data in the table represent the mean values of five independent experiments, with standard deviations as follows: Precision (RT-DETR: \(\pm 0.008\), EER-DETR: \(\pm 0.006\)); Recall (RT-DETR: \(\pm 0.009\), EER-DETR: \(\pm 0.007\)); mAP50 (RT-DETR: \(\pm 0.007\), EER-DETR: \(\pm 0.005\)). Paired \(t\)-test shows that EER-DETR is significantly superior to RT-DETR in Precision (t = 4.21, \(p<0.05\)), Recall (t = 3.12, \(p<0.05\)), and mAP50 (t = 5.12, \(p<0.01\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Precision** & **Recall** & **mAP50** & **mAP50-90** & **Parameters** & **GFLOPs** \\ \hline RT-DETR & 0.893 & 0.841 & 0.886 & 0.456 & 19,875,612 & 56.9 \\ EER-DETR & 0.916 & 0.872 & 0.905 & 0.458 & 17,941,933 & 48.6 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison with the original model. Figure 7: The comparison chart of detection performance between EER-DETR and RT-DETR. We also compared EER-DETR with the classic YOLO series models. From Table 3, we can see that EER-DETR demonstrated significant advantages in multiple indicators such as Precision, Recall, mAP50, and mAP50-90. In terms of Precision, EER-DETR reached 0.909; this indicates that EER-DETR has higher accuracy in target recognition. In terms of Recall, EER-DETR reached 0.872, while the best-performing model in the YOLO series, YOLOv5, was 0.865. This shows that EER-DETR has better comprehensiveness in target detection.