Abstract: The scene information of image is more abundant in the air-to-ground perspective, which is helpful for the location and classification of target. The traditional single shot multibox detector(SSD) net predicts bounding boxes and classifications of the target from six feature maps, ignoring the auxiliary effect of high-level semantic feature maps on the ?ne details of shallow layers. In order to combine scene information, firstly, the effects of different scale feature maps on target detection are analyzed on the basis of SSD net. And then a new scene auxiliary structure is established by combining the ideas of feature pyramid networks and long short term memory net to enhance representation of feature maps. At last, the method is validated on a self-made dataset of air-to-ground and compared with several classic networks in the detection domain. The results show that the proposed method has higher detection accuracy than others, which can detect targets efficiently under the prerequisite of guaranteeing speed.