Two-stream network for online quadrotor detection without dedicated annotations

Ruibing Jin, Jianliang Wang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Aerial vehicles detection, especially quadrotor detection, is important for cooperative unmanned aerial vehicles (UAV). However, traditional object detection approaches require a well-annotated and large scale dataset which is labor-intensive and time-consuming. Since the appearance of UAV is various, it is difficult to establish a dataset covering all kinds of UAVs. An intuitive solution for it is to train a network on an off-the-shelf dataset of a class which is under the same parent category as quadrotors. However, domain gap between these two classes hinders the performance of the trained network. To address this issue, a two-stream network is proposed. The appearance information (from a spatial stream) and the motion information (from a temporal stream) are incorporated in this network. A fusion module, cross proposal module, is proposed to fuse these two streams. To verify the performance of this two-stream network, a fully annotated dataset of quadrotors is established. Extensive experiments are conducted on it and the results show that our two-stream network performs better than traditional detection approaches in this task.

Original languageEnglish
Title of host publication2019 IEEE 15th International Conference on Control and Automation, ICCA 2019
PublisherIEEE Computer Society
Pages561-566
Number of pages6
ISBN (Electronic)9781728111643
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event15th IEEE International Conference on Control and Automation, ICCA 2019 - Edinburgh, United Kingdom
Duration: 16 Jul 201919 Jul 2019

Publication series

NameIEEE International Conference on Control and Automation, ICCA
Volume2019-July
ISSN (Print)1948-3449
ISSN (Electronic)1948-3457

Conference

Conference15th IEEE International Conference on Control and Automation, ICCA 2019
Country/TerritoryUnited Kingdom
CityEdinburgh
Period16/07/1919/07/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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