# DAC-SDC Low Power Object Detection Challenge for UAV Applications

Source: [DAC-SDC Low Power Object Detection Challenge for UAV Applications](<https://www.sota2.com/research/paper/68fa64c71e209131cea0689b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:36.700Z

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

Published: 2018-09-01T03:37:37.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1809.00110>)

[PDF](<https://arxiv.org/pdf/1809.00110.pdf>)