15 FPS, 3$\times$ faster than the Faster R-CNN counterpart. Code will be available at <https://github.com/taokong/RON>.
author:
- |
    Tao Kong$^{1*}$    Fuchun Sun$^1$    Anbang Yao$^2$    Huaping Liu$^1$    Ming Lu$^3$   Yurong Chen$^2$\
    $^1$State Key Lab. of Intelligent Technology and Systems, Tsinghua National Laboratory for\
    Information Science and Technology (TNList), Department of Computer Science and Technology,\
    Tsinghua University $^2$Intel Labs China $^3$Department of Electronic Engineering, Tsinghua University\
    $^{1}${kt14@mails,fcsun@,hpliu@}.tsinghua.edu.cn\
    $^2${anbang.yao, yurong.chen}@.intel.com    $^3$lu-m13@mails.tsinghua.edu.cn
bibliography:
- 'egbib.bib'
title: |
    RON: Reverse Connection with Objectness Prior Networks\
    for Object Detection
---

Introduction
============

![Objectness prior generated from a specific image. In this example, sofa is responded at scales (a) and (b), the brown dog is responded at scale (c) and the white spotted dog is responded at scale (d). The network will generate detection results with the guidance of objectness prior .[]{data-label="pipline"}](objectness.pdf){width="0.8\linewidth"}

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We are witnessing significant advances in object detection area, mainly thanks to the deep networks. Current top deep-networks-based object detection frameworks could be grouped into two main streams: the region-based methods [@girshick2014rich][@fasterrcnn][@frcnn][@hypernet] and the region-free methods [@yolo][@ssd].

The region-based methods divide the object detection task into two sub-problems: At the first stage, a dedicated region proposal generation network is grafted on 