## 1 Introduction
The YOLOv9e model demonstrated superior performance with an F1 score of 86% and a mAP of 74% at an IoU range of 0.5 to 0.95, surpassing the capabilities of the YOLOv9c model. Compared to YOLOv8x [27], YOLOv9e exhibited a 15% diminution in the number of parameters, a 25% reduction in the requisite computational demands, and a 1.7% enhancement in AP [24]. [PERSON] et al. 's CNNs have been effectively used for fault detection in solar panels, achieving a success rate of 91.1% for binary classification and 88.6% for multi-classification of defects such as shadow, cracked, or dusty cells [28]. [PERSON] et al. 's hybrid CNN-GAN model improved detection accuracy to 93% for dust classification on solar panels, outperforming traditional CNN methods [29]. [PERSON] et al. 's CNNs combined with data augmentation strategies have shown an F1-score of 88% and an accuracy of 87.64% in defect classification, demonstrating the effectiveness of data augmentation in enhancing model performance [30]. [PERSON] et al. 's lack of publicly available datasets for solar panel defects remains a significant challenge, necessitating the development of tailored algorithms for small-sample scenarios. Techniques like R-CDT transformation and Faster R-CNN have been proposed to address these challenges, showing high effectiveness in defect recognition [31]. Future research is directed toward integrating these models with Internet of Things (IoT) technology for self-maintaining solar systems, which could revolutionize maintenance approaches and enhance the utilization of renewable solar power [29]. This research hypothesizes that deep learning-based models, particularly YOLOv8-m, YOLOv9-e, and a k-fold cross-validated CNN, can accurately classify solar panels as broken, clean, or dirty with high precision and reliability. By leveraging advanced neural network architectures and data augmentation techniques, these models will outperform traditional manual inspection methods and contribute to the automation of solar panel maintenance, ultimately enhancing energy efficiency. In this study, traditional pre-trained models (VGG16, MobileNetV2, etc.) were tested first, but the focus was shifted to YOLO-based architectures due to their higher accuracy and real-time adaptability. In addition to YOLO models, a novel CNN architecture was designed to improve classification performance, and the results of this model were reported with k-fold cross-validation. A unique and proprietary dataset was developed exclusively for this study to test this hypothesis. The dataset under scrutiny consists of 6079 high-resolution images of solar panels, meticulously categorized into three distinct groups: broken (2245 images), clean (2130 images), and dirty (1704 images). In contrast to publicly available datasets, this dataset was meticulously curated to encompass a wide range of environmental conditions, diverse lighting scenarios, and varied panel orientations, thereby ensuring its applicability to real-world conditions. Furthermore, data augmentation techniques such as rotation, shifting, shearing, zooming, and flipping were employed to enhance model performance and mitigate class imbalance. This methodological approach enhances the extraction of features and concomitantly improves the generalizability capability of the models across a range of conditions. The dataset's distinctiveness and comprehensive nature position it as a significant resource for advancing automated defect detection in photovoltaic systems.