Vehicle detection in remote sensing images plays an important role for its wide range of applications. However, it is still a challenging task due to their small sizes. In this paper, we propose an efficient single-shot-based detector, called SuperDet, which achieves a combination of a super resolution algorithm with a deep convolutional neural network (DCNN)-based object detector. In SuperDet, there are two interconnected modules, namely, the super resolution module and the vehicle detection module. The super resolution module aims to recover a high resolution sensing image from its low resolution counterpart. With this module, the small vehicles will have a higher resolution, which is helpful for their detection. Taking the higher resolution image as input, the vehicle detection module extracts the features and predicts the location and category of the vehicles. We use a multi-task loss function to train the network in an end-to-end way. To assess the detection performance of SuperDet, we conducted experiments between SuperDet and the classical object detectors on both VEDAI and DOTA datasets. Experimental results indicate that SuperDet outperforms other detectors for vehicle detection in remote sensing images.