## 4 Discussion  
The findings of this study demonstrate the effectiveness of deep learning models, particularly YOLO-based architectures, in classifying solar panel conditions. The YOLOv8-m model achieved the highest accuracy (97.26%) and specificity (95.94%), outperforming both YOLOv9-e (95.18% accuracy) and the CNN model with k-fold cross-validation (92.86% accuracy). These results align with recent advancements in object detection models, where YOLO variants have shown superior performance in real-time applications due to their optimized architecture for balancing speed and accuracy. For instance, [PERSON] et al. reported a 91.8% [EMAIL_ADDRESS] for defect detection using YOLOv8, which is consistent with our findings, further validating the robustness of YOLO-based approaches in solar panel inspection tasks [12]. The proposed model performed well, as observed in Table 8, where similar studies are also mentioned. This comparison highlights its superiority in solar panel defect detection, achieving a higher accuracy (97.26%) compared to other state-of-the-art methods like YOLOv8 (94%) and YOLOv9 (96%). Such advancements underscore the potential of YOLOv8-m as a leading solution for automated inspection systems. A key strength of this study lies in the use of a novel, proprietary dataset comprising 6079 images under diverse environmental conditions. Unlike publicly available datasets, this dataset was meticulously curated to include variations in lighting, angles, and panel orientations, enhancing its applicability to real-world scenarios. Data augmentation techniques, such as rotation, shifting, and flipping, were critical in addressing class imbalance, particularly for the underrepresented \"broken\" class. This approach not only improved model generalization but also reduced overfitting risks, as evidenced by the high sensitivity (100%) of YOLOv8-m and YOLOv9-e models. The superior performance of YOLO models over the CNN can be attributed to their inherent design for object detection tasks. YOLO architectures excel in localizing and classifying objects within images, making them inherently suitable for identifying defects like cracks or dirt on solar panels. In contrast, while the CNN model demonstrated strong generalization through k-fold cross-validation, its lower accuracy (92.86%) highlights the challenges traditional CNNs face in handling spatially complex defects without explicit localization mechanisms. This aligns with [PERSON] et al. (2024), who noted that hybrid models (e.g., CNN-GAN) could enhance performance, suggesting potential avenues for future CNN-based improvements [19]. The developed novel CNN architecture achieved 92.86% accuracy with k-fold cross-validation and produced competitive results compared to similar CNN structures in the literature, despite its low performance compared to YOLO-based models. Notably, the YOLOv8-m model's specificity (95.94%) and precision-recall scores (97.10%) indicate its reliability in minimizing false positives, a critical factor for automated inspection systems where misclassifications could lead to unnecessary maintenance costs. The model's stability during training, as reflected in its consistent loss reduction and  
[PERSON]begin{table} \begin{tabular}{c c c} \hline \hline
**Author(s)** & **Method** & **Accuracy** \\ \hline [PERSON] et al. [19] & YOLOv5 and YOLOv9 & 70.4\% \\ [PERSON] et al. [22] & YOLOv9 andYOLOv10 & 74–73\% \\ [PERSON] et al. [16] & YOLOv8 & 94\% \\ [PERSON] et al. [21] & YOLOv9 & 96\% \\ SPHERE (Proposed Research) & YOLOv8-m & 97.26\% \\ \hline \hline \end{tabular} 
\end{table}
Table 8: Comparison of this research with related works. validation metrics (Figures 8 and 10), further underscores its suitability for deployment in resource-constrained environments. The confusion matrix analysis revealed that the 'dirty' class was misclassified as 'broken'. These errors are due to the visual similarity of dark spots to micro-cracks, light reflections creating misleading impressions, and loss of fine detail in low-resolution images.