The thermal target information of infrared image and some detail information of visible images are usually ignored in image fusion method based on generative adversarial network. To address the problem， an infrared and visible image fusion method based on dual-path cascade adversarial mechanism is proposed. In the stage of the generator model， a dual-path cascade is used to extract features of infrared and visible images， respectively. To improve the quality of fusion， structural similarity is introduced into the loss function. In the stage of the discriminator model， a dual discriminator is used to distinguish the generated image from the true natural visual images. The proposed method is experimented on the public data， and compared with eight state-of-the-art image fusion methods. The experimental results show that the fusion image not only retains more the target information of infrared images， but also retains more detail information of visible images， which is superior to state-of-the-art methods in subjective evaluation and objective assessment.