# Lem-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
## 4 Experiments
### Ablation Studies  
We perform ablation experiments on different components of the proposed LEM-Detector, LFA, EFEM, and MMFPN, to verify the effectiveness of those components.  
Figure 8: P-R curve of LEM-Detector.  
From Table 4, it is evident that the use of LFA for enhancing linear features, the EFEM for accurately extracting multi-scale feature information, and the MMFPN module for capturing often-overlooked edge information significantly improves detection accuracy. The overall mAP reaches 94.7%, with each module contributing to enhanced experimental precision. Notably, the detection results for Crack and Star crack types show substantial improvements, with accuracy increases of 12.1 and 9.3 percentage points, respectively, compared to RT-DETR, effectively demonstrating the detection efficacy of the LEM-Detector.  
Using the TIDE evaluation metric, as shown in Table 5, it is evident that our detection model exhibits significant enhancements in various aspects, particularly in localization errors (Loc), duplicate detection errors (Dupe), background errors (Bkg), and missed ground truths (Miss). The model's performance in terms of false positives (FPs) and false negatives (FNs) is also markedly superior to that of the RT-DETR model, with only minor deficiencies in classification errors (Cls) and errors involving both classification and localization (Both). Notably, substantial improvements are observed in localization (Loc) and background (Bkg) errors, attributed to our precise retention of defect boundary information during feature fusion. The MMFPN effectively reduces the risk of overfitting by integrating complementary information from different feature layers, enhancing the model's generalization capabilities. Importantly, our model did not exhibit any missed GT errors (Miss), indicating that the EFEM effectively captures small-scale defect information without omissions, thereby improving the model's performance in multi-scale defect detection.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**LFA** & **EFEM** & **MMFPN** & **[EMAIL_ADDRESS] (\%)** & **Ck** & **Fr** & **Bc** & **TI** & **Sc** & **Hd** & **Vd** & **St** \\ \hline \multirow{3}{*}{\(\surd\)} & & & 91.3 & 72.6 & 94.5 & 98.9 & 90.0 & 82.7 & **99.5** & 92.7 & **99.5** \\  & & & 92.5 & 78.2 & 95.0 & 99.3 & 91.5 & 86.4 & **99.5** & 90.8 & **99.5** \\  & \(\surd\) & & 92.2 & 77.9 & 94.2 & 98.8 & 89.3 & 85.2 & 99.4 & 93.0 & **99.5** \\  & & \(\surd\) & 92.6 & 76.4 & 93.9 & 99.1 & 90.5 & 84.7 & **99.5** & **98.4** & 98.6 \\ \(\surd\) & \(\surd\) & & 93.1 & 79.6 & **94.5** & **99.4** & 90.3 & 86.2 & **99.5** & 96.1 & **99.5** \\ \(\surd\) & & \(\surd\) & 92.9 & 78.7 & 94.4 & 99.3 & 90.9 & 86.9 & 99.4 & 94.5 & **99.5** \\  & \(\surd\) & \(\surd\) & 92.8 & 76.9 & 94.3 & 98.3 & 88.9 & 88.5 & **99.5** & 97.1 & **99.5** \\ \(\surd\) & \(\surd\) & \(\surd\) & **94.7** & **84.7** & 94.3 & 98.8 & **91.1** & **92.0** & **99.5** & 97.7 & **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 ($t).  
\end{table}
Table 4: The results of the LEM-Detector with different components on the PVEL-AD dataset.  
Figure 9: Detection results of LEM-Detector.