Although deep learning based vehicle detection approaches have achieved remarkable success recently, they are still likely to miss comparatively small-sized vehicle. To address this problem, we propose a novel Dilated Fully Convolutional Network with Grouped Proposals (DFCN-GP) for vehicle detection. Specifically, we invented a grouped network structure to combine feature maps from both lower and higher level convolutional layers for the generation of object proposal and focusing more on lower level features, which are more sensitive to discovering small object. In addition, we increase the size and reception field of the feature map in the last convolutional layers to keep more detailed information via dilated convolution, which is used in both object proposal and vehicle detection sub-networks. In the experiment, we conducted ablation studies to demonstrate the effectiveness of the grouped proposals and dilated convolutional layer. We also show that the proposed approach outperforms other state-of-the-art methods on the UA-DETRAC vehicle detection.