In air-to-ground remote sensing detection, the object has the characteristics of small field of view and single viewing angle, which is susceptible to background interference. At the same time, the height of the field of view varies greatly, which brings challenges to the traditional deep learning detection algorithm. To solve the problem, a scene-coupled multi-task object detection algorithm is proposed. First, a new scene-coupled object detection network structure is designed, which mirrors and fuses the scene classification feature map and the object detection feature map on the same scale to enrich the fine-grain of the feature description. Second, a differentiated activation module is designed to realize the importance screening of feature channels. Then, the optimization function of multi-task coupling is derived, which can simultaneously optimize the scene classification loss and object detection loss. Finally, an air-to-ground detection multi-task dataset is established to verify the effectiveness of proposed method. The experimental results show that the proposed algorithm effectively improves the accuracy and robustness of air-to-ground small object detection, and can adapt to different heights to identify multi-task requirements, which provides a new idea and method for space-based unmanned platform intelligent detection.