# 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
In terms of mAP50 and mAP50-90, EER-DETR reached 0.905 and 0.458, respectively, while the best-performing model in the YOLO series, YOLOv5, reached 0.888 and 0.446, respectively. This indicates that EER-DETR has superior comprehensive performance under different detection difficulties. In contrast, the YOLO series models have shortcomings in some aspects. For example, YOLOv7 performed poorly in Precision and Recall, with 0.862 and 0.743, respectively, indicating that its accuracy and comprehensiveness in target recognition need to be improved. YOLOv10 also performed poorly in Precision and Recall, with 0.851 and 0.795, respectively, indicating that its performance in target detection needs to be enhanced. As shown in Tables 4 and 5, we tested the performance of various models on the photovoltaic panel defect dataset. However, in general, none of them outperformed our designed EER-DETR model. Firstly, regarding different backbone structures, after improving RT-DETR with Swin-Transformer, although it has higher parameters and computational complexity, it still performs reasonably well in Precision, Recall, mAP50, and mAP50-90 indicators. This indicates that it has certain advantages in feature extraction and object detection. However, compared with EER-DETR, SwinTransformer is slightly inferior in these indicators, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Precision** & **Recall** & **mAP50** & **mAP50-90** & **Parameters** & **GFLOPs** \\ \hline SwinTransformer & 0.894 & 0.839 & 0.886 & 0.441 & 36,319,810 & 97.0 \\ VanillaNet & 0.871 & 0.842 & 0.861 & 0.440 & 21,716,360 & 66.8 \\ StarNet & 0.855 & 0.827 & 0.835 & 0.436 & 11,994,248 & 31.8 \\ ConvNextV2 & 0.862 & 0.831 & 0.841 & 0.444 & 12,306,808 & 31.9 \\ EER-DETR & 0.916 & 0.872 & 0.905 & 0.458 & 17,941,933 & 48.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative experiments with different main structures replaced. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **mAP50** & **mAP50-90** \\ \hline Yolov5 & 0.891 & 0.865 & 0.888 & 0.446 \\ Yolov7 & 0.862 & 0.743 & 0.775 & 0.366 \\ Yolov8 & 0.879 & 0.776 & 0.856 & 0.453 \\ Yolov9 & 0.903 & 0.868 & 0.902 & 0.484 \\ Yolov10 & 0.851 & 0.795 & 0.848 & 0.445 \\ Yolov11 & 0.871 & 0.821 & 0.875 & 0.463 \\ EER-DETR & 0.921 & 0.872 & 0.905 & 0.458 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with classical algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Precision** & **Recall** & **mAP50** & **mAP50-90** & **Parameters** & **GFLOPs** \\ \hline SlimNeck & 0.871 & 0.838 & 0.863 & 0.446 & 19,304,872 & 53.3 \\ MAFPN & 0.878 & 0.847 & 0.871 & 0.447 & 22,932,584 & 56.3 \\ BIFPN & 0.894 & 0.853 & 0.882 & 0.456 & 20,306,612 & 64.3 \\ EER-DETR & 0.916 & 0.872 & 0.905 & 0.458 & 17,941,933 & 48.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experiments on replacing different encoder networks. especially in Precision and mAP50. EER-DETR achieved 0.909 and 0.905, respectively, while SwinTransformer only reached 0.864 and 0.852. This shows that EER-DETR is more accurate and has better detection performance. Models such as VanillaNet, StarNet, and ConvNextV2, which are improved models, have relatively lower parameters and computational complexity, but they still have a gap compared to EER-DETR in Precision, Recall, mAP50, and mAP50-90 indicators. Although VanillaNet is slightly higher than SwinTransformer in Precision and mAP50, it does not show obvious advantages in other indicators. This indicates that VanillaNet may have certain potential from some aspects, but its overall performance still needs further optimization. Improvements and comparisons on different structures of the neck part were also made. After improving RT-DETR with SlimNeck, its parameters were reduced and the computational complexity was lower. However, there is still a gap in terms of the Precision, Recall, mAP50 and mAP50-90 indicators compared with EER-DETR.