Applying deep learning to enhance person detection in maritime images

The application of sensor data obtained from patrol ships, drones, and specific coastal locations may contribute to the development of effective and scalable monitoring systems for enhancing coastal security and maritime domain awareness. Typically, daytime surveillance relies on high-resolution images captured by visible sensors, whereas infrared imaging can be employed under low-visibility conditions. In this study, we focus on a critical aspect of maritime surveillance: deep learning-based person detection. The collected datasets included visible and infrared images of passengers on ships, offshore wind turbine decks, and people in water. In addition, vessel classification was considered. To exploit both spectral domains, we applied a preprocessing strategy to the thermal data, transforming the infrared images to resemble the visible ones. We fine-tuned the detector using this data. Our findings show that the deep learning model can effectively distinguish between human and vessel signatures, despite challenges such as low pixel resolution, cluttered backgrounds, and varying postures of individuals. Moreover, our results suggest that the extracted features from the infrared data significantly improve the detector’s performance in the visible domain by using appropriate preprocessing techniques. However, we observed a limited transferability of models that have been pre-trained on visible images to the infrared spectral domain.