Unmanned Aerial Vehicle （UAV） aerial images have a wide field of vision， and the targets in the images are small and have blurred boundaries. And the existing Single Shot multibox Detector （SSD） target detection model is difficult to accurately detect small targets in aerial images. In order to effectively solve the problem that the original model is easy to have missed detection， based on Feature Pyramid Network （FPN）， a new SSD model based on continuous upsampling was proposed. In the improved SSD model， the input image size was adjusted to \mathrm{320} \times \mathrm{320} ， the Conv3_3 feature layer was added， the high-level features were upsampled， and features of the first five layers of VGG16 network were fused by using feature pyramid structure， so as to enhance the semantic representation ability of each feature layer. Meanwhile， the size of anchor box was redesigned. Training and verification were carried out on the open aerial dataset UCAS-AOD. Experimental results show that， the improved SSD model has 94.78% in mean Average Precision （mAP） of different categories， and compared with the existing SSD model， the improved SSD model has the accuracy increased by 17.62%， including 4.66% for plane category and 34.78% for car category.