In order to improve the accuracy and real-time performance of infrared image target detection, an infrared target fusion detection algorithm based on pseudo modal transformation is proposed. First, the pseudo visible image corresponding to the infrared image is obtained by using the advantage of dual cycle generation confrontation without training image scene matching; then, the residual network is constructed to extract the features of the dual-mode image, and the feature vector is fused by the add superposition method, and the rich semantic information of the visible image is used to make up for the lack of the target information of the infrared image, so as to improve detection accuracy. Finally, considering the target detection efficiency, three scales of dual-mode targets are predicted by using the YOLOv3 single-stage detection network, and the targets are classified by using the logistic expression model. Experimental results show that the algorithm can effectively improve the accuracy of target detection.