DAC-SDC Low Power Object Detection Challenge for UAV Applications

Xiaowei Xu; Xinyi Zhang; Bei Yu; X. Sharon Hu; Christopher Rowen; Jingtong Hu; Yiyu Shi

DAC-SDC Low Power Object Detection Challenge for UAV Applications

Computer Vision and Pattern Recognition 2018-09-05 v1

Authors: Xiaowei Xu , Xinyi Zhang , Bei Yu , X. Sharon Hu , Christopher Rowen , Jingtong Hu , Yiyu Shi

Abstract

The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018. SDC'18 features a lower power object detection challenge (LPODC) on designing and implementing novel algorithms based object detection in images taken from unmanned aerial vehicles (UAV). The dataset includes 95 categories and 150k images, and the hardware platforms include Nvidia's TX2 and Xilinx's PYNQ Z1. DAC-SDC'18 attracted more than 110 entries from 12 countries. This paper presents in detail the dataset and evaluation procedure. It further discusses the methods developed by some of the entries as well as representative results. The paper concludes with directions for future improvements.

Keywords

image processing competition

Cite

@article{arxiv.1809.00110,
  title  = {DAC-SDC Low Power Object Detection Challenge for UAV Applications},
  author = {Xiaowei Xu and Xinyi Zhang and Bei Yu and X. Sharon Hu and Christopher Rowen and Jingtong Hu and Yiyu Shi},
  journal= {arXiv preprint arXiv:1809.00110},
  year   = {2018}
}

Comments

12 pages, 21 figures

DAC-SDC Low Power Object Detection Challenge for UAV Applications

Abstract

Keywords

Cite

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