# Lem-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
## 4 Experiments
### Ablation Studies
#### 4.5.2 The Effect of EFEM  
In contrast to the BasicBlock module used for feature extraction in ResNet, which can be insensitive to information variations due to shared parameters in convolutional operations, the proposed EFEM effectively addresses this limitation. By emphasizing the importance of different features within the receptive field and prioritizing spatial characteristics, EFEM enriches the diversity of feature expression. It selectively enhances meaningful features while suppressing redundant or irrelevant ones, enabling the network to capture precise local information and represent complex features more effectively. This approach incurs negligible computational costs and parameter increments, significantly enhancing network performance. The improved results are presented in Table 7.  
To visualize the effectiveness of the proposed EFEM compared to the BasicBlock module used in RT-DETR, GradCAM++ was employed to extract heatmaps from the outputs of the feature extraction modules at the same layer. Figure 10 presents these heatmaps, with the first column displaying the original images, the second column showing  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS]** & **Sample Size** & **Ck** & **Fr** & **Bc** & **TI** & **Sc** & **Hd** & **Vd** & **St** \\ \hline RT-DETR & 91.3 & **3151** & 72.6 & 94.5 & 98.9 & 90.0 & 82.7 & **99.5** & **92.7** & **99.5** \\ RT-DETR + DA & 91.7 & 12,470 & 77.6 & 93.6 & 98.4 & 91.3 & **86.5** & **99.5** & 87.4 & **99.5** \\ RT-DETR + LFA & **92.5** & 8761 & **78.2** & **95.0** & **99.3** & **91.5** & 86.4 & **99.5** & 90.8 & **99.5** \\ \hline \hline \end{tabular} Note: Crack (Ck), Finger (Fr), Black core (Bc), Thick line (TI), Star crack (Sc), Horizontal dislocation (Hd), Vertical dislocation (Vd), and Short circuit (St).  
\end{table}
Table 6: Ablation experiment of LFA (DA denotes the use of the same data augmentation methods as in LFA, but without processing through the cosine similarity threshold.).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS]** & **Cls** & **Loc** & **Both** & **Dupe** & **Bkg** & **Miss** & **FP** & **FN** \\ \hline RT-DETR & 91.30 & 0.48 & 1.67 & 0.15 & 0.43 & 2.56 & 0.14 & 6.84 & 1.29 \\ LEM-Detector & 94.70 & 0.55 & 1.02 & 0.18 & 0.13 & 1.99 & 0.00 & 4.40 & 0.66 \\ Improvement & **+3.40** & \(-0.07\) & **+0.65** & \(-0.03\) & **+0.30** & **+0.57** & **+0.14** & **+2.44** & **+0.63** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Distribution of error types.  
the features extracted using the BasicBlock module, and the third column illustrating the features extracted by the EFEM.  
The first and second rows of Figure 10 demonstrate that for the challenging cases of Crack and Star crack defects (which typically exhibit lower detection accuracy), the EFEM is more effective at capturing precise information regarding the dispersion of cracks compared to the features extracted by BasicBlock. Furthermore, in the third and fourth rows, even under challenging conditions such as the low brightness of defect samples and defects crossing over busbars, the EFEM demonstrates accurate identification capabilities. These visual comparisons highlight the ability of the EFEM to capture more meaningful and precise features, contributing to the enhanced performance of the LEM-Detector.