Beyond RGB: Early Stage Fusion of Thermal and Visual Modalities for Robust Maritime Perception

In maritime environments, reliable object detection and semantic segmentation are essential for navigation and collision avoidance, especially under adverse conditions. This paper benchmarks early stage RGB–thermal (RGBT) fusion architectures for these tasks using a novel, pixel-aligned maritime dataset. We evaluate transformer-based, attention-driven, and lightweight convolutional models, analyzing trade-offs between accuracy and efficiency for edge deployment. Our results show that RGBT fusion significantly improved detection robustness, with transformer models achieving the top accuracy and lightweight models like WNet-S offering strong performance with lower computational costs. We also introduce a modular, open-source fusion framework to support reproducible research and practical deployment in maritime and other safety-critical domains.