A marine infrared target detection algorithm based on improved YOLOx-nano is proposed. By decoupling the classification and positioning tasks of the detection head and introducing an improved feature pyramid network (FPN) structure, not only the accuracy and convergence speed of the model are improved, but also the infrared large target detection capability is improved. The improved squeeze-and-excitation network (SENet) channel attention mechanism module is added to the model to enhance the nonlinear expression ability of the model and improve the effective feature learning ability. In order to speed up the forward reasoning speed of the embedded platform model, the pruning technology is introduced to implement model pruning, and the model parameters are reduced without reducing the recall rate. The algorithm in this paper is verified by the test set. The results show that the average precision (AP) of the algorithm is 1.35% higher than that of the original YOLOx-nano algorithm, reaching 93.92%. The algorithm in this paper balances the contradictory relationship between model accuracy and time consumption, and ensures the speed of model detection while improving performance.