# Lem-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
## 4 Experiments
### The Result of Defect Detection  
The performance of the proposed LEM-Detector is compared to related methods in Figure 7. The LEM-Detector achieves a mean Average Precision (mAP) of 94.7%, representing a 3.4 percentage point improvement over RT-DETR. Compared to Faster R-CNN and RetinaNet [38], our model achieves an accuracy improvement of over 8 percentage points while utilizing fewer parameters. When compared to the latest YOLOv10 [39], which has a similar number of parameters, our model surpasses YOLOv10 by 6% in terms of mAP. These results highlight the significant performance gains achieved by the LEM-Detector, demonstrating its effectiveness in addressing the challenges of multi-scale defect detection in photovoltaic panels. Table 2 provides a detailed view of the superior performance of the LEM-Detector. Compared to existing methods, the LEM-Detector, utilizing LFA, EFEM, and MMFPN, achieves an optimal balance among model size, detection speed, and GFLOPs while ensuring top detection accuracy, making it especially suitable for industrial inspection applications. Table 3 further reveals significant performance gains for the Crack and Star crack defect types, which typically exhibit lower detection accuracy. This improvement is attributed to the integration of the LFA specifically designed for linear crack data. The LFA creates a more realistic dataset with more evenly distributed training samples, enhancing the model's ability to accurately identify these challenging defects. The combination of the EFEM and the Multi-Scale Multi-Feature Fusion Pyramid Network MMFPN ensures that each feature layer retains rich scale information while preserving defect feature information outside the candidate boxes. This approach reduces information coupling and enhances edge information, leading to improved detection \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS] (\%)** & **Parameter (M)** & **GFLOPs** & **FPS** \\ \hline Faster-RCNN & 85.8 & 261.4 & 137.0 & 28 \\ RetinaNet & 86.5 & 190.9 & 98.0 & 32 \\ Deformable-DETR & 90.1 & 40.1 & 126.0 & 37 \\ DAB-DETR & 90.6 & 44.0 & 94.0 & 31 \\ YOLOv8n & 89.6 & **2.9** & **8.1** & 88 \\ RT-DETR & 91.3 & 18.9 & 57.0 & **260** \\ YOLOv9c & 88.7 & 24.7 & 102.1 & 61 \\ YOLOv10x & 89.9 & 28.0 & 160.0 & 54 \\ LEM-Detector & & **94.7** & 33.8 & 67.2 & 60 \\ (ours) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with advanced object detection detectors (all object detectors were trained starting from the smallest model until they were able to detect all defect types. ). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Ck** & **Fr** & **Bc** & **T1** & **Sc** & **Hd** & **Vd** & **St** & **[EMAIL_ADDRESS]** \\ \hline Faster-RCNN & 59.8 & 87.1 & 95.5 & 78.7 & 71.0 & **99.5** & 95.5 & 85.8 \\ RetinaNet & 59.3 & 88.8 & 97.7 & 81.2 & 68.8 & **99.5** & 97.5 & **99.5** & 86.5 \\ Deformable-DETR & 73.2 & 93.6 & 99.1 & 89.8 & 81.1 & 96.9 & **97.7** & **99.5** & 90.1 \\ DAB-DETR & 70.7 & 87.3 & 98.5 & 89.6 & 82.7 & **99.5** & 97.6 & **99.5** & 90.6 \\ YOLOv8n & 72.8 & 93.8 & **99.2** & 84.1 & 83.0 & 98.0 & 87.2 & 99.2 & 89.6 \\ RE-DETR & 72.6 & **94.5** & 98.9 & 90.0 & 82.7 & **99.5** & 92.7 & **99.5** & 91.3 \\ YOLOv9c & 68.0 & 93.1 & 98.4 & 83.1 & 76.3 & 98.4 & 92.7 & **99.5** & 88.7 \\ YOLOv10x & 72.0 & 91.5 & 98.5 & 84.4 & 79.1 & **99.5** & 95.8 & 98.6 & 89.9 \\ LEM-Detector & & & & & & & & \\ (ours) & & & & & & & & \\ \hline \hline \end{tabular} Note: Crack (Ck), Finger (Fr), Black core (Bc), Thick line (Tl), Star crack (Sc), Horizontal dislocation (Hd), Vertical dislocation (Vd), and Short circuit (St).  
\end{table} 
Table 3: [EMAIL_ADDRESS] for different defect types. Figure 7: The proposed LEM-Detector achieves state-of-the-art performance when compared to existing prominent object detectors. accuracy. The LEM-Detector's high detection accuracy further validates the effectiveness of the LFA, EFEM, and MMFPN in multi-scale defect detection of photovoltaic panel images.