HybridNet: A fast vehicle detection system for autonomous driving

作者：

Highlights：

• In this paper, a new two-stage regression based cascade object detection system is proposed.

• This system can be fast detection of the vehicles which concentrated the advantages of the single-stage and two-stage methods, denoted by HybridNet.

• In our design, the first and the second stage are both regression modes.

• We add a transitional stage to map proposals(generated in the first stage) on high resolution feature maps to get exact features for decision refinement in the second stage.

• The challenging KITTI and PASCAL VOC2007 data sets are used to evaluate our proposed method.

• The experimental results show that our approach is more fast and more accurate in vehicle detection than other state-of-the-art methods.

摘要

•In this paper, a new two-stage regression based cascade object detection system is proposed.•This system can be fast detection of the vehicles which concentrated the advantages of the single-stage and two-stage methods, denoted by HybridNet.•In our design, the first and the second stage are both regression modes.•We add a transitional stage to map proposals(generated in the first stage) on high resolution feature maps to get exact features for decision refinement in the second stage.•The challenging KITTI and PASCAL VOC2007 data sets are used to evaluate our proposed method.•The experimental results show that our approach is more fast and more accurate in vehicle detection than other state-of-the-art methods.

论文关键词：Vehicle detection,CNN,Two-stage,Decision refinement

论文评审过程：Received 18 April 2018, Revised 4 September 2018, Accepted 4 September 2018, Available online 22 September 2018, Version of Record 1 October 2018.

论文官网地址：https://doi.org/10.1016/j.image.2018.09.002