ShipNet: High-Resolution Ship Detection Using Convolutional Neural Networks

Accurate and efficient detection of ships in high-resolution optical images is critical for maritime surveillance, port management, and environmental monitoring. This research proposes ShipNet: High-Resolution Ship Detection Using Convolutional Neural Networks (CNNs), a deep learning framework designed to automatically identify and extract ships from satellite and aerial imagery with high precision. The proposed model leverages a multi-layer CNN architecture to learn hierarchical features directly from raw optical images, enabling robust recognition of ships under varying scales, orientations, and environmental conditions such as shadows, waves, and occlusions. To enhance detection accuracy, a post-processing module refines the CNN output by reducing false positives and improving boundary delineation of detected ships. The system is trained and validated using annotated datasets of high-resolution maritime images and evaluated with metrics including precision, recall, F1-score, and Intersection over Union (IoU). Comparative analysis with traditional image processing techniques and standard CNN-based detection models demonstrates that ShipNet achieves superior detection performance, particularly in cluttered or complex maritime environments. Experimental results indicate that the proposed framework not only provides accurate and reliable ship localization but also supports real-time processing, making it suitable for practical applications in maritime traffic monitoring, port security, and disaster management. The study underscores the potential of deep learning approaches to transform remote sensing applications by enabling automated, scalable, and high-fidelity ship detection in optical imagery.