DAC-SDC Low Power Object Detection Challenge for UAV Applications

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

arxiv: 1809.00110 · v1 · pith:HMNNTQ6Fnew · submitted 2018-09-01 · 💻 cs.CV

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 This is my paper

classification 💻 cs.CV

keywords detectionobjectchallengedac-sdcdatasetdesignentriesimages

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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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SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection
cs.CV 2019-06 unverdicted novelty 4.0

SkyNet, a lightweight 12-layer DNN with 1.82 MB parameters, won first place in the DAC-SDC low-power UAV object detection contest, achieving 0.731 IoU at 67.33 FPS on TX2 GPU and 0.716 IoU at 25.05 FPS on Ultra96 FPGA.