## 4 Discussion
Suggested improvements to address these issues include the following:  
* Attention mechanisms such as CBAM to focus the model on critical regions (panel cell boundaries) [51]. * Synthetic data augmentation simulating combinations of dirt and cracks. * Temperature-based discrimination enhancement by multi-spectral analysis integrating thermal and RGB data. * Dual-stream CNN architecture that processes textural and structural features in parallel. * Integration of sub-models highlighting micro-cracks. In addition, it is aimed to reduce false positives by post-processing filtering (hotspot detection) based on thermal data. These steps can significantly improve the model's reliability in industrial applications by reducing the error rate. The primary reason for the superior performance of YOLOv8-m (97.26% accuracy) over YOLOv9-e (95.18% accuracy) is significant disparities in architectural design and feature processing strategies. The YOLOv8-m model has been demonstrated to achieve high levels of accuracy in detecting both minor and complex defects (i.e., micro-cracks) by its CSPDarknet-based backbone structure, multi-scale feature fusion with the SPPF module, and dynamic anchor-free mechanism. In contrast, although YOLOv9-e incorporates advanced techniques such as transformer-based layers and adaptive feature selection, these features increase the risk of overfitting at limited data scales. They may result in the loss of critical details in complex backgrounds (e.g., dirt-shadow blending). Furthermore, the architecture of YOLOv8-m is simpler and more stable, enabling faster convergence in the training process. In contrast, the gradient-guided optimization of YOLOv9-e increases the computational burden. Consequently, the practice-oriented design (SPPF, dynamic anchor) of YOLOv8-m offers enhanced performance in heterogeneous environments, such as solar panels. In order to unlock the potential of YOLOv9-e, integration with larger datasets or attention mechanisms can be proposed. In this study, the performance of the CNN model was evaluated using 9-fold cross-validation, while the YOLO models were tested using a fixed data split (70% training, 20% validation, and 10% testing). The direct comparability of these methodologies is questionable. Cross-validation measures have been shown to generalize performance more reliably, whereas fixed splits carry the risk of dependency on a particular data distribution. However, 9-fold cross-validation is not a viable option due to the substantial training times and resource constraints of YOLO models. This finding aligns with the conclusions of analogous studies documented in the extant literature [19]. In order to provide a fair comparison, the fixed division-trained version of the CNN model was also tested, with 92.86% accuracy being obtained. This result is lower than the 97.26% accuracy of YOLOv8-m measured with fixed splitting, thus supporting the architectural superiority of YOLO. Moreover, given the real-time performance (45 FPS) and object localization capabilities of YOLO models, the results obtained with fixed splitting remain valid in practical applications. However, certain limitations must be acknowledged. The dataset, though diverse, was limited to three classes (broken, clean, and dirty). Expanding the classification to include subcategories of defects (e.g., micro-cracks and delamination) or environmental factors (e.g., snow and shading) could improve practical utility. Additionally, while data augmentation mitigated class imbalance, the relatively smaller size of the \"broken\" class (2245 images vs. 2130 clean and 1704 dirty) may still introduce subtle biases. Future studies could explore synthetic data generation or adversarial training to further address this issue. The integration of these models with IoT-enabled autonomous systems, as suggested by [PERSON] et al., represents a promising direction for real-time monitoring and maintenance [6].