Abstract: Aiming at the difficulty of infrared target detection in sea scenes with different fluctuation degree， an algorithm based on dual modeling for sea background in the Fourier domain is proposed. Different sea scenes have different fluctuation degree， and it is difficult to accurately describe all the sea scenes with a single model. The stability of the seawater’s amplitude spectrum in the Fourier domain is strong. Therefore， a dual seawater background model in the Fourier domain， namely mixture Gaussian model and probabilistic single Gaussian model， is proposed. Firstly， Fourier transformation in the row direction is performed for maritime infrared images to obtain the amplitude spectrum and phase spectrum. Next， the variance of the amplitude spectrum sequence of pure seawater images is used to identify the fluctuation degree of seawater in the training stage. If the seawater fluctuates violently， the probabilistic single Gaussian model in the Fourier domain is used to model the seawater. If the seawater fluctuates gently， the mixture Gaussian model in the Fourier domain is used to model the seawater. By comparing the amplitude spectrum of the test frame with the sea background model， the detection results are obtained. The experimental results show that the algorithm can effectively suppress the sea background and accurately detect the target in different sea scenes.