Ship Detection

Ship detection from satellite and aerial imagery is a crucial task with applications in maritime surveillance, environmental monitoring, and security. Current research focuses on improving the accuracy and efficiency of detection, particularly for small or obscured vessels, using advanced deep learning models such as variations of convolutional neural networks (CNNs), transformers, and hybrid architectures incorporating attention mechanisms. These models are being optimized for resource-constrained environments (e.g., onboard satellites) and adapted to handle diverse image sources (optical, radar, infrared) and challenging conditions (e.g., cluttered backgrounds, varying weather). The resulting improvements in ship detection technology have significant implications for various sectors, including improved maritime safety, more effective enforcement of regulations, and enhanced environmental protection.