Open Access
ARTICLE
Object Detection and Fuzzy-Based Classification Using UAV Data
Abdul Qayyum1,*, Iftikhar Ahmad2, Mohsin Iftikhar3, Moona Mazher4
1 mViA laboratory, University of Bourgogne Franche-Comté, 21000, Dijon, France
2 Department of Software Engineering, College of Computer and Information Sciences, P.O. Box 51178, Riyadh 11543, King Saud University, Riyadh, KSA
3 Centre School of Computing and mathematics, Charles Sturt University, Australia
4 Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia.
* Corresponding Author: Abdul Qayyum,
Intelligent Automation & Soft Computing 2020, 26(4), 693-702. https://doi.org/10.32604/iasc.2020.010103
Abstract
UAV (Unmanned Aerial Vehicle) equipped with remote sensing devices can
acquire spatial data with a relevant area of interest. In this paper, we have
acquired UAV data for high voltage power poles, urban areas and
vegetation/trees near power lines. For object classification, the proposed
approach based on the fuzzy classifier is compared with the traditional
minimum distance classifier and maximum likelihood classifier on our three
defined segments of UAV images. The performance evaluation of all the
classifiers was based on the statistics parameters which included the mean,
standard deviation and PDF (probability density function) of each object present
in the image acquired by the UAV and the variances of each channel of the UAV
imagery were calculated. The results showed that the fuzzy-based classifier
outperformed as compared to the other classifiers. We achieved the
classification accuracy of 93% with a Fuzzy-based classifier.
Keywords
Cite This Article
A. Qayyum, I. Ahmad, M. Iftikhar and M. Mazher, "Object detection and fuzzy-based classification using uav data,"
Intelligent Automation & Soft Computing, vol. 26, no.4, pp. 693–702, 2020.
Citations