Use of Particle Swarm Optimization in Terrain Classification based on UAV Downwash

Iuliia Kim, J. P. Matos-Carvalho, Ilya Viksnin, Luis Miguel Campos, José M. Fonseca, Andre Mora, Sergey Chuprov

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

1 Citation (Scopus)

Abstract

Nowadays, the number of aerial unmanned vehicles (UAVs) is growing at a tremendous speed, as well as its technology. Therefore, it is essential to follow this growth with increasingly robust algorithms to be possible to exist cooperation between robots autonomously. One of the major events currently being developed in autonomous cooperation is relatively terrain classification where, this classification, is mainly important for emergency landings, mapping and decision making. This paper presents a robust computer vision system to sort terrain types using two main algorithms: Particle Swarm Optimization (PSO) and Gray-Level Co-Occurrence Matrix (GLCM). In addition to these two algorithms, a neural network was designed with the aim of increasing the probability of success of the proposed system. In order to evaluate this article, the system is validated using videos acquired onboard of a UAV with a RGB camera.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages604-610
Number of pages7
ISBN (Electronic)9781728121536
DOIs
Publication statusPublished - Jun 2019
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
CountryNew Zealand
CityWellington
Period10/06/1913/06/19

Keywords

  • computer vision
  • glcm
  • image processing
  • particle swarm optimization
  • terrain classification
  • unmanned aerial vehicle

Fingerprint

Dive into the research topics of 'Use of Particle Swarm Optimization in Terrain Classification based on UAV Downwash'. Together they form a unique fingerprint.

Cite this