<![CDATA[As the photovoltaic (PV) systems expands globally, robust defect detection and precise localization technologies becomes crucial to ensure their operational efficiency. This review introduces an integrated deep learning framework that leverages Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and You Only Look Once (YOLO) algorithms to enhance defect detection in solar panels. By integrating these technologies with Global Positioning System (GPS) and Real-Time Kinematic (RTK) GPS, the framework achieves unprecedented accuracy in defect localization, facilitating efficient maintenance and monitoring of expansive solar farms. Specifically, CNNs are employed for their superior feature detection capabilities in identifying defects such as microcracks and delamination. RNNs enhance the framework by analyzing time-series data from panel sensors, predicting potential failure points before they manifest. YOLO algorithms are utilized for their real-time detection capabilities, allowing for immediate identification and categorization of defects during routine inspections. This review's novel contribution lies in its use of an integrated approach that combines these advanced technologies to not only detect but also accurately localize defects, significantly impacting the maintenance strategies for PV systems. The findings demonstrate an improvement in detection speed and localization accuracy, suggesting a promising direction for future research in solar panel diagnostics. The review provided aims to refine surveillance systems and improve the maintenance protocols for photovoltaic installations, ensuring longevity, durability and efficiency in energy production.]]>
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