Compared with the traditional detectors, the detectors based on large data and deep learning do not require manually designed features and are more robust. Under the background of air defense, we build the images and videos dataset of aerial target for training and test, improve the deep learning-based detector Faster R-CNN, and specialize it in aerial target detection. Aiming at the peculiarities and requirements of aerial target detection, we propose the strategies such as accumulation of dilation, regional amplification, local tagging, adaptive threshold and spatio-temporal context to make up the shortage of Faster R-CNN that small weak or occluded targets can not be detected and improve the detection speed and accuracy. Experimental results show that the improved Faster R-CNN performs well under circumstances such as small weak or multiple targets, clutter, illumination changes, blur and large-area occlusion. Compared to the original Faster R-CNN, the mean average precision is improved by 16.7% on the built dataset, and the speed is 3 times faster.