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Traditional ship detection methods struggle with the challenges posed by the vastness, complexity, and dynamic nature of oceanic environments. To address these challenges, we propose a highly efficient model that combines the power of CNN with the RPN for fast and accurate ship detection. This study introduces a novel approach for real-time ship object detection in RSI images using the Faster R-CNN.\n\nThe proposed model is optimized for real-time processing, ensuring the rapid detection of ships even in large-scale satellite imagery. By utilizing a comprehensive dataset of high-resolution RSI images, the system is trained to recognize ships under varying conditions, including different angles, lighting, and sea states. The Faster R-CNN model demonstrates superior performance, offering both high detection accuracy and fast inference times, making it suitable for deployment in real-world maritime surveillance systems. 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