## 3 Results
### YOLOv8
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Score of Class** & **Precision–Recall** & **F1-Score** & **Precision** & **Recall** \\ \hline \multirow{4}{*}{Nano} & class score 0 & 0.992 & - & - & - \\  & class score 1 & 0.965 & - & - & - \\  & class score 2 & 0.947 & - & - & - \\  & all classes & 0.968 [EMAIL_ADDRESS] & 0.91 at 0.489 & 1.00 at 1.000 & 1.00 at 1.000 \\ \hline \multirow{4}{*}{Small} & class score 0 & 0.993 & - & - & - \\  & class score 1 & 0.965 & - & - & - \\  & class score 2 & 0.956 & - & - & - \\  & all classes & 0.971 [EMAIL_ADDRESS] & 0.93 at 0.447 & 1.00 at 1.000 & 1.00 at 1.000 \\ \hline \multirow{4}{*}{Medium} & class score 0 & 0.993 & - & - & - \\  & class score 1 & 0.967 & - & - & - \\  & class score 2 & 0.952 & - & - & - \\  & all classes & 0.971 [EMAIL_ADDRESS] & 0.93 at 0.401 & 1.00 at 1.000 & 1.00 at 1.000 \\ \hline \multirow{4}{*}{Large} & class score 0 & 0.993 & - & - & - \\  & class score 1 & 0.967 & - & - & - \\  & class score 2 & 0.952 & - & - & - \\  & all classes & 0.971 [EMAIL_ADDRESS] & 0.93 at 0.401 & 1.00 at 1.000 & 1.00 at 1.000 \\ \hline \multirow{4}{*}{X-large} & class score 0 & 0.992 & - & - & - \\  & class score 1 & 0.965 & - & - & - \\ \cline{1-1}  & class score 2 & 0.947 & - & - & - \\ \cline{1-1}  & all classes & 0.971 [EMAIL_ADDRESS] & 0.93 at 0.401 & 1.00 at 1.000 & 1.00 at 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training parameters for YOLOv8 based on model weights. The confusion matrices for the YOLOv8-s and YOLOv8-m models are provided in Figures 5 and 6, offering a comprehensive visualization of their classification performance. In these matrices, the false negative (FN) values are represented within the background row, whereas the false positive (FP) values are positioned in the background column. The true positive (TP) values indicate correctly classified instances and are located along the diagonal axis extending from the top-left to the bottom-right of the matrix. The true negative (TN) values are also derived based on Figure 2.  
Figure 4: F1–confidence curve for YOLOv8-s.  
Figure 5: Confusion matrix for YOLOv8-s.  
For the YOLOv8-s model, the performance metrics computed from the confusion matrix indicate that the sensitivity (recall) is 1.00, meaning the model successfully detects all relevant instances without missing any positive cases. The specificity, which measures the model's ability to identify negative cases correctly, is 0.9504, reflecting its effectiveness in minimizing false positives. Lastly, the overall accuracy of the YOLOv8-s model is calculated as 0.9659, demonstrating its high reliability in distinguishing between the three solar panel conditions. Similarly, for the YOLOv8-m model, the corresponding values derived from its confusion matrix indicate that the sensitivity remains at 1.00, confirming that this model, like YOLOv8-s, does not miss any positive instances. However, the specificity of YOLOv8-m is slightly higher at 0.9594, suggesting a marginal improvement in correctly identifying negative cases. Additionally, the accuracy of the YOLOv8-m model is recorded as 0.9726, showing that it slightly outperforms the YOLOv8-s model in overall classification performance. These findings highlight that while both models demonstrate strong predictive capabilities, the YOLOv8-m model exhibits a slight edge in specificity and overall accuracy. The results pertaining to the accuracy, specificity, and sensitivity of YOLOv8-s and YOLOv8-m are displayed in Table 3. In addition, Figures 5 and 6 show the confusion matrix of YOLOv8-s and YOLOv8-m respectively. Although the sensitivity values of both the small and medium YOLOv8 models are comparable, the medium model demonstrates slightly higher accuracy and specificity \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline YOLOv8-s & 96.59\% & 100\% & 95.04\% \\ YOLOv8-m & 97.26\% & 100\% & 95.94\% \\ \hline \hline \end{tabular}
\end{table}