Object detection based on computer vision is becoming popular in drone-captured images. However, real-time object detection in unmanned aerial vehicle (UAV) scenarios is a huge challenge for low-end devices. To deal with the problem, we have improved YOLOv3-tiny in the following aspects. First, the label rewriting problem, which is caused by network structure and dataset of YOLOv3-tiny in drone-captured images detection, is very serious. The method of increasing the size of the predicted feature map is used to reduce the ratio of label rewriting. Second, the features of small targets will be reduced in a small feature map, but the context information with large receptive fields in it can improve the performance of small target detection. So we use dilated convolution to expand the receptive field without reducing the size of the feature map. Third, multiscale feature fusion is very helpful for small target detection. The multidilated module is adopted to merge features in earlier layer and deeper layers. Finally, a pretraining strategy combining copy-paste data augmentation method is proposed to learn more features from categories with a small number of samples. We evaluated our model on the VisDrone2019-Det test set. It achieves compelling results compared to the counterparts of YOLOv3-tiny, including ∼86.1 % decline in model size, increasing ∼19.2 % AP50. Although our model is slower than YOLOv3-tiny, it is 2.96 times faster than YOLOv3. The results of experiments verify that our network is more effective than YOLOv3-tiny. It is more suitable for UAV object detection applications on low-end devices.