Abstract: With the rapid development of the photovoltaic industry, the health status of photovoltaic modules is crucial for power generation efficiency. This article proposes a photovoltaic module crack detection system based on artificial intelligence image processing and drone technology. The system adopts convolutional neural network for automatic crack detection and classification. Through experimental verification, its average crack detection rate reaches 92.8%, which meets the efficient operation and maintenance needs of large-scale photovoltaic power plants. The research results show that the system has significant advantages in improving the efficiency and accuracy of photovoltaic module fault detection, providing reliable support for the intelligent development of the photovoltaic industry.