This paper provides a comprehensive survey of existing methods for detecting overlays and faults in photovoltaic (PV) panels. PV panels are prone to overlays such as dust, snow, and shading, which reduce their efficiency and performance. Faults such as cracks, hot spots, and partial shading also affect the reliability and lifespan of PV systems. Detecting these issues is crucial for maintaining system efficiency, reducing maintenance costs, and ensuring sustainable energy production. The paper classifies existing overlay detection methods into two categories: image processing and deep learning. Image processing methods use techniques like grayscale transformation, histogram equalization, and edge detection to identify overlays. Deep learning methods, on the other hand, use neural networks to automatically detect and classify overlays. Both methods have their advantages and limitations, with image processing being more efficient and accurate in some cases, while deep learning offers higher accuracy and adaptability. For fault detection, the paper categorizes methods into three types: electrical parameter detection, image processing-based detection, and data mining and AI-based detection. Each method has its own strengths and weaknesses, with electrical parameter detection being simple but less accurate, while AI-based methods offer higher accuracy but require more computational resources. The paper also discusses the factors that influence overlay and fault detection, including weather conditions, lighting, types of overlays, geographic location, camera parameters, and detection algorithms. These factors can significantly affect the accuracy and effectiveness of detection methods. The paper highlights the importance of regular maintenance and cleaning of PV panels to prevent efficiency loss and damage. It also discusses the challenges of manual cleaning, such as high costs and resource consumption, and emphasizes the need for automated and efficient detection methods. The paper reviews various image processing techniques for overlay detection, including the use of drones, satellites, and high-resolution cameras. It also discusses the advantages and limitations of deep learning methods, including their high accuracy and ability to handle complex data. The paper concludes that while image processing and deep learning methods are effective for overlay and fault detection, there are still challenges to overcome, such as the need for large datasets, high computational costs, and the impact of environmental factors on detection accuracy. Future research should focus on improving these methods to enhance the efficiency and reliability of PV systems.This paper provides a comprehensive survey of existing methods for detecting overlays and faults in photovoltaic (PV) panels. PV panels are prone to overlays such as dust, snow, and shading, which reduce their efficiency and performance. Faults such as cracks, hot spots, and partial shading also affect the reliability and lifespan of PV systems. Detecting these issues is crucial for maintaining system efficiency, reducing maintenance costs, and ensuring sustainable energy production. The paper classifies existing overlay detection methods into two categories: image processing and deep learning. Image processing methods use techniques like grayscale transformation, histogram equalization, and edge detection to identify overlays. Deep learning methods, on the other hand, use neural networks to automatically detect and classify overlays. Both methods have their advantages and limitations, with image processing being more efficient and accurate in some cases, while deep learning offers higher accuracy and adaptability. For fault detection, the paper categorizes methods into three types: electrical parameter detection, image processing-based detection, and data mining and AI-based detection. Each method has its own strengths and weaknesses, with electrical parameter detection being simple but less accurate, while AI-based methods offer higher accuracy but require more computational resources. The paper also discusses the factors that influence overlay and fault detection, including weather conditions, lighting, types of overlays, geographic location, camera parameters, and detection algorithms. These factors can significantly affect the accuracy and effectiveness of detection methods. The paper highlights the importance of regular maintenance and cleaning of PV panels to prevent efficiency loss and damage. It also discusses the challenges of manual cleaning, such as high costs and resource consumption, and emphasizes the need for automated and efficient detection methods. The paper reviews various image processing techniques for overlay detection, including the use of drones, satellites, and high-resolution cameras. It also discusses the advantages and limitations of deep learning methods, including their high accuracy and ability to handle complex data. The paper concludes that while image processing and deep learning methods are effective for overlay and fault detection, there are still challenges to overcome, such as the need for large datasets, high computational costs, and the impact of environmental factors on detection accuracy. Future research should focus on improving these methods to enhance the efficiency and reliability of PV systems.