Maritime Multimodal Data Resource System—Infrared-visible Dual-modal Dataset for Ship Detection

A maritime multimodal data resource system provides a foundation for multisensor collaborative detection using radar, Synthetic Aperture Radar (SAR), and electro-optical sensors, enabling fine-grained target perception. Such systems are essential for advancing the practical application of detection algorithms and improving maritime target surveillance capabilities. To this end, this study constructs a maritime multimodal data resource system using multisource data collected from the sea area near a port in the Bohai Sea. Data were acquired using SAR, radar, visible-light cameras, infrared cameras, and other sensors mounted on shore-based and airborne platforms. The data were labeled by performing automatic correlation registration and manual correction. According to the requirements of different tasks, multiple task-oriented multimodal associated datasets were compiled. This paper focuses on one subset of the overall resource system, namely the Dual-Modal Ship Detection, which consists exclusively of visible-light and infrared image pairs. The dataset contains 2163 registered image pairs, with intermodal alignment achieved through an affine transformation. All images were collected in real maritime environments and cover diverse sea conditions and backgrounds, including cloud, rain, fog, and backlighting. The dataset was evaluated using representative algorithms, including YOLO and CFT. Experimental results show that the dataset achieves an mAP@50 of approximately 0.65 with YOLOv8 and 0.63 with CFT, demonstrating its effectiveness in supporting research on optimizing bimodal fusion strategies and enhancing detection robustness in complex maritime scenarios.