Dyfusion: Dynamic IR/RGB Fusion for Maritime Vessel Recognition

We propose a novel multi-sensor data fusion approach called DyFusion for maritime vessel recognition using long-wave infrared and visible images. DyFusion consists of a decision-level fusion of convolutional networks using a probabilistic model that can adapt to changes in the scene. The probabilistic model avails of contextual clues from each sensor decision pipeline to maximize accuracy and to update probabilities given to each sensor pipeline. Additional sensors are simulated by applying simple transformations on visible images. Evaluation is presented on the VAIS dataset, demonstrating the effectiveness and robustness of DyFusion with a reliable accuracy of up to 88% in hard scenarios.