## 1 Introduction  
Solar energy is critical in meeting global energy needs as a rapidly growing sector among renewable energy sources [1]. However, the efficiency of solar panels can be significantly reduced due to surface contamination, mechanical damage, or environmental factors [2]. Since traditional manual inspection methods are time-consuming, costly, and prone to human error, the need for automated image-based systems is increasing [3]. Interest in renewable energy sources is increasing due to fossil fuels' environmental impacts and exhaustibility. In this context, photovoltaic (PV) systems offer an important alternative for clean and sustainable energy generation. However, factors such as snow, dust, shadows, bird droppings, etc., on PV panels reduce energy production efficiency and require regular maintenance. Since traditional manual maintenance methods are time-consumingand error-prone, there is a need for automated and reliable fault detection systems [4]. Solar panels have seen a 50% increase in their role in the electricity grid, reaching almost 510 gigawatts (GW) in 2023, representing the most rapid growth rate observed over the past two decades [1]. Solar panels have been identified as playing a crucial role in generating electricity [5]. Nevertheless, the presence of defects in solar panels can result in considerable power loss. These faults can be classified into two categories: electrical and nonelectrical. Among the nonelectrical defects, inadequate construction and improper panel selection, often resulting from root growth, as well as settling of dust and dirt, browning of the panels, shading due to overgrowth of vegetation, discoloration and delamination, and clamping-induced failures have been identified. The prevalence of these defects often leads to hotspots forming within the panels. On the other hand, electrical defects encompass a range of problems, from line-to-line faults and ground-related issues to open circuit faults. The root causes of these electrical defects are frequently attributed to excessive connections and soldering points [5]. A promising approach to addressing these defects involves using Autonomous Monitoring Systems (AMSs), which have proven effective in identifying localized defects in solar panels and facilitating timely and targeted interventions [5]. The extant literature documents the development of a range of traditional image processing and Artificial Intelligence (AI) algorithms for real-time defect detection in solar panels. Nevertheless, there is scope for improvement in enhancing these algorithms' performance. Higher accuracy in defect detection can result in a reduction in overall expenses and a saved input of resources [6; 7; 8; 9; 10; 11]. Classifying solar panel conditions is paramount for effectively maintaining photovoltaic systems, ensuring optimal efficiency and longevity. Recent advancements in deep learning models, including YOLOv8, YOLOv9, and Convolutional Neural Networks (CNNs), have demonstrated considerable potential in automating the detection and classification of defects in solar panels. This study aims to provide a comprehensive overview of the performance and applications of these models in solar panel condition classification. [PERSON] et al. proposed that the YOLOv8 model has been optimized for detecting micro defects in solar cells using infrared images from UAVs. The ESD-YOLOv8 variant, which includes architectural optimizations and attention mechanisms, achieved a high [EMAIL_ADDRESS] of 91.8% and [EMAIL_ADDRESS]:0.95 of 58.0%, indicating efficient real-time detection capabilities [12]. [PERSON] et al. suggested that the YOLOv8-seg model achieved a mean average precision (mAP) of 87% for segmenting solar panels. In comparison, the YOLOv8m model reached an mAP of 76% for damage detection, demonstrating its effectiveness in classifying distinct damage classes [13]. [PERSON] et al. propose a deep-learning model, termed DEA-YOLOv9, for the detection of defects in photovoltaic modules.