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A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

  • Zhaowei CaiEmail author
  • Quanfu Fan
  • Rogerio S. Feris
  • Nuno Vasconcelos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9908)

Abstract

A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network. In the proposal sub-network, detection is performed at multiple output layers, so that receptive fields match objects of different scales. These complementary scale-specific detectors are combined to produce a strong multi-scale object detector. The unified network is learned end-to-end, by optimizing a multi-task loss. Feature upsampling by deconvolution is also explored, as an alternative to input upsampling, to reduce the memory and computation costs. State-of-the-art object detection performance, at up to 15 fps, is reported on datasets, such as KITTI and Caltech, containing a substantial number of small objects.

Keywords

Object detection Multi-scale Unified neural network 

Notes

Acknowledgements

This work was partially funded by NSF grant IIS1208522 and a gift from KETI. We also thank NVIDIA for GPU donations through their academic program.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zhaowei Cai
    • 1
    Email author
  • Quanfu Fan
    • 2
  • Rogerio S. Feris
    • 2
  • Nuno Vasconcelos
    • 1
  1. 1.SVCLUC San DiegoSan DiegoUSA
  2. 2.IBM T. J. Watson ResearchYorktown HeightsUSA

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