ColANet: A UAV Collision Avoidance Dataset

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

6 Citations (Scopus)

Abstract

Artificial Intelligence is evolving at an accelerating pace alongside the increasing number of large datasets due to vast number of image data on the Internet. Unnamed Aircraft Vehicles (UAVs) are also a new trend that will have a huge impact over the next years. The use of UAVs arises some safety issues, such as collisions with dynamic obstacles like birds, other planes, or random thrown objects. Those are complex and sometimes impossible to avoid with state-of-the-art algorithms, representing a threat to the applications. In this article, a new video dataset of collisions, entitled ColANet, aims to provide a base for training new Machine Learning algorithms for handling the problem of avoiding collisions with high efficiency and robustness. It is also shown that using this dataset is easy to build new neural network models and test them.

Original languageEnglish
Title of host publicationTechnological Innovation for Life Improvement - 11th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020, Proceedings
EditorsLuis M. Camarinha-Matos, Nastaran Farhadi, Fábio Lopes, Helena Pereira
Place of PublicationCham
PublisherSpringer
Pages53-62
Number of pages10
ISBN (Electronic)978-3-030-45124-0
ISBN (Print)978-3-030-45123-3
DOIs
Publication statusPublished - 2020
Event11th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020 - Costa de Caparica, Portugal
Duration: 1 Jul 20203 Jul 2020

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSpringer
Volume577
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference11th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020
CountryPortugal
CityCosta de Caparica
Period1/07/203/07/20

Keywords

  • Artificial Intelligence
  • Collision avoidance
  • Dataset
  • Machine Learning
  • Neural network
  • Safety
  • UAS
  • UAV

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