DAC-SDC Low Power Object Detection Challenge for UAV Applications
September 01, 2018 Β· Declared Dead Β· π IEEE Transactions on Pattern Analysis and Machine Intelligence
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Authors
Xiaowei Xu, Xinyi Zhang, Bei Yu, X. Sharon Hu, Christopher Rowen, Jingtong Hu, Yiyu Shi
arXiv ID
1809.00110
Category
cs.CV: Computer Vision
Citations
89
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Last Checked
3 months ago
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.
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