Limited by the carrying capacity of image spectral information, single-mode target detection methods based on visible light or infrared are usually difficult to effectively deal with complex scenes of remote sensing images. Aiming at this problem, a super-resolution remote sensing image target detection method based on hierarchical fusion mechanism is proposed, which effectively fuses visible light and infrared data information. Firstly, the residual fusion module and the single-branch module are used to construct a hierarchical fusion mechanism. The residual fusion module combines the potential complementary information of visible and infrared images, and the single-branch module enhances the single-modal features and assists in enhancing the dual-modal data fusion feature expression. Secondly, in order to solve the problem of missing target details in low-resolution images, a super-resolution auxiliary branch is introduced to enhance the ability of target detail feature generation and further improve the detection accuracy. The experimental results show that the detection accuracy (mAP₅₀) of the proposed method on the VEDAI and Drone Vehicle datasets is better than that of the existing target detection methods, reaching 79.45% and 81.29%, which effectively improves the accuracy and robustness of remote sensing image target detection in complex environments.