University of Oulu

Pham, G. N., Nguyen, P. H., Drone detection experiment based on image processing and machine learning, International Journal of Scientific and Technological Research, ISSN: 2277-8616, Vol. 9:2, p. 2965-2971, http://www.ijstr.org/research-paper-publishing.php?month=feb2020

Drone detection experiment based on image processing and machine learning

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Author: Pham, Giao N.1; Nguyen, Phong H.2
Organizations: 1Dept. of Computing Fundamentals, FPT University, Hanoi, Vietnam
2Center of Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020062245143
Language: English
Published: Amazedia Solutions, 2020
Publish Date: 2020-06-22
Description:

Abstract

Drones are widely used in the field of information gathering and tracking, even they could be used to attacked targets. Therefore, the drone detection for the restricted areas or special zones is important and necessary. This paper focuses on the drone detection problem based on image processing for the restricted areas or special zones where used cameras for monitoring. The proposed solution detects drones from the captured images based on training the Haar-like features. The dataset of drone images is used in the Haar training process to generate a Haar-cascade model of drones. This model is then used to detect drones from images captured by the camera. The proposed solution is implemented and experimented with single cameras installed for any place including indoor environment and outdoor environment. Experimental results proved that the proposed solution could exactly detect drones for any zone or the restricted areas. The average accuracy of the proposed solution in the experimented environments is 91.9 %, and it provides an easy and economical solution for user.

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Series: International journal of scientific & technological research
ISSN: 2277-8616
ISSN-E: 2277-8616
ISSN-L: 2277-8616
Volume: 9
Issue: 2
Pages: 2965 - 2971
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © International Journal of Scientific and Technological Research 2020. Published here with the kind permission by the Editor-in-Chief Dr. J. N. Swaminathan and the Publisher Amazedia Solutions, India.