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

doi:10.1109/CEC.2019.8790031

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 proceeding › Conference contribution › peer-review

2 Citations (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 language	English
Title of host publication	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	604-610
Number of pages	7
ISBN (Electronic)	9781728121536
DOIs	https://doi.org/10.1109/CEC.2019.8790031
Publication status	Published - Jun 2019
Event	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand Duration: 10 Jun 2019 → 13 Jun 2019

Conference

Conference	2019 IEEE Congress on Evolutionary Computation, CEC 2019
Country/Territory	New Zealand
City	Wellington
Period	10/06/19 → 13/06/19

Keywords

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

Access to Document

10.1109/CEC.2019.8790031

Cite this

Kim, I., Matos-Carvalho, J. P., Viksnin, I., Campos, L. M., Fonseca, J. M., Mora, A., & Chuprov, S. (2019). Use of Particle Swarm Optimization in Terrain Classification based on UAV Downwash. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 604-610). Article 8790031 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC.2019.8790031

@inproceedings{b3e65e7b31454f3ab3e060e8313ae094,

title = "Use of Particle Swarm Optimization in Terrain Classification based on UAV Downwash",

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.",

keywords = "computer vision, glcm, image processing, particle swarm optimization, terrain classification, unmanned aerial vehicle",

author = "Iuliia Kim and Matos-Carvalho, {J. P.} and Ilya Viksnin and Campos, {Luis Miguel} and Fonseca, {Jos{\'e} M.} and Andre Mora and Sergey Chuprov",

note = "This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783221. The JU receives support from the European Union's Horizon 2020 research and innovation programme and Austria, Belgium, Czech Republic, Finland, Germany, Greece, Italy, Latvia, Norway, Poland, Portugal, Spain, Sweden. This work was also a fusion work between the Center of Technologies and System (CTS) of UNINOVA - Institute for the Development of new Technologies; Faculty of Secure Information Technologies ITMO University. This research was also financially supported by Government of Russian Federation, Grant 074-U01.; 2019 IEEE Congress on Evolutionary Computation, CEC 2019 ; Conference date: 10-06-2019 Through 13-06-2019",

year = "2019",

month = jun,

doi = "10.1109/CEC.2019.8790031",

language = "English",

pages = "604--610",

booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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Kim, I, Matos-Carvalho, JP, Viksnin, I, Campos, LM, Fonseca, JM , Mora, A & Chuprov, S 2019, Use of Particle Swarm Optimization in Terrain Classification based on UAV Downwash. in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings., 8790031, Institute of Electrical and Electronics Engineers (IEEE), pp. 604-610, 2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 10/06/19. https://doi.org/10.1109/CEC.2019.8790031

Use of Particle Swarm Optimization in Terrain Classification based on UAV Downwash. / Kim, Iuliia; Matos-Carvalho, J. P.; Viksnin, Ilya et al.
2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 604-610 8790031.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Kim, Iuliia

AU - Matos-Carvalho, J. P.

AU - Viksnin, Ilya

AU - Campos, Luis Miguel

AU - Fonseca, José M.

AU - Mora, Andre

AU - Chuprov, Sergey

N1 - This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783221. The JU receives support from the European Union's Horizon 2020 research and innovation programme and Austria, Belgium, Czech Republic, Finland, Germany, Greece, Italy, Latvia, Norway, Poland, Portugal, Spain, Sweden. This work was also a fusion work between the Center of Technologies and System (CTS) of UNINOVA - Institute for the Development of new Technologies; Faculty of Secure Information Technologies ITMO University. This research was also financially supported by Government of Russian Federation, Grant 074-U01.

PY - 2019/6

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N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85071297288

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EP - 610

BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019

Y2 - 10 June 2019 through 13 June 2019

ER -

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

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