# EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR
## 5 Discussion and Conclusions  
To significantly enhance the accuracy and real-time performance of photovoltaic panel defect detection, thereby providing strong technical support for the intelligent operation and maintenance of photovoltaic systems, this study proposes an improved photovoltaic panel defect detection method, EER-DETR, based on RT-DETR. Through in-depth experimental verification of this algorithm, the results show that the designed EER-DETR model exhibits excellent performance in photovoltaic panel defect detection, achieving a significant improvement in detection accuracy, while significantly reducing the computational cost. The EER-DETR algorithm adds a structural re-parameterization module, WDBB, to form a new feature extraction network, RepaNet and designs an efficient multi-scale convolution module and global heterogeneous kernel selection mechanism, EHFPN, to reduce computational overhead; introducing EUCB to optimize the upsampling process not only retains more details when improving image resolution but also enables the network to effectively learn small target features at low resolutions, thereby reducing the problem of missed detections and improving the accuracy and robustness of photovoltaic panel defect detection. This optimization makes the algorithm more stable and efficient in practical applications, especially in the defect detection tasks of large-scale photovoltaic power stations, where it demonstrates stronger adaptability compared to traditional methods. However, this study still has certain limitations. Although data augmentation strategies were employed, in extremely complex environmental conditions (such as extreme lighting), the model's detection accuracy for some minor defects may decline, and there is a risk of overfitting. Through qualitative analysis of the detection results, it was found that in some cases, when the light is uneven or the edge defect features are blurry, the model may have false detections or missed detections. For example, when the scratch defect is too similar to the texture of the panel itself, the model has difficulty accurately distinguishing them, resulting in detection errors. There are also deficiencies in the detection of some internal issues. These situations indicate that the model's robustness in handling complex visual information still needs to be improved. EER-DETR as an innovative method for detecting defects in photovoltaic panels, not only providing a new solution for intelligent operation and maintenance from a theoretical perspective but also demonstrating strong application potential in practice, indicating its \begin{table}
\begin{tabular}{c c c c c} \hline  & **Resolution** & **FPS** & **VRAM Usage (MB)** & **Power Consumption** \\ \hline RT-DETR & 640 \(\times\) 640 & 42.8 & 746 & 7.8 \\ EER-DETR & 640 \(\times\) 640 & 36.5 & 599 & 6.9 \\ \hline \end{tabular}
\end{table} 
Table 7: The deployment on the NVIDIA Jetson Nano platform. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Precision** & **Recall** & **mAP50** & **mAP50-90** & **Parameters** & **GFLOPs** \\ \hline RT-DETR & 0.893 & 0.842 & 0.885 & 0.453 & 19,908,612 & 57.0 \\ EER-DETR & 0.915 & 0.868 & 0.901 & 0.457 & 17,987,586 & 48.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The test on the PVEL-AD dataset. broad application prospects in the future photovoltaic industry. Looking to the future, it is recommended to conduct in-depth research in the following directions: firstly, expand the range of detectable defects to include more rare defect types, and enhance the model's generalization ability; secondly, integrate multi-spectral data and utilize the feature information under different spectral conditions to improve the ability to identify complex defects; finally, further optimize the domain adaptation algorithm to ensure that the model maintains stable and efficient detection performance in different environments and scenarios, promoting the continuous progress of photovoltaic panel defect detection technology. Conceptualization, Y.T. and J.D.