  Although some convolutional neural networks (CNNs) based super-resolution
(SR) algorithms yield good visual performances on single images recently. Most
of them focus on perfect perceptual quality but ignore specific needs of
subsequent detection task. This paper proposes a simple but powerful
feature-driven super-resolution (FDSR) to improve the detection performance of
low-resolution (LR) images. First, the proposed method uses feature-domain
prior which extracts from an existing detector backbone to guide the HR image
reconstruction. Then, with the aligned features, FDSR update SR parameters for
better detection performance. Comparing with some state-of-the-art SR
algorithms with 4$\times$ scale factor, FDSR outperforms the detection
performance mAP on MS COCO validation, VOC2007 databases with good
generalization to other detection networks.
