An automatic visual inspection of oil tanks exterior surface using unmanned aerial vehicle with image processing and cascading fuzzy logic algorithms

Mohammed A. H., Ali and Muhammad, Baggash and Jaloliddin, Rustamov and Abdulghafor, Rawad Abdulkhaleq Abdulmolla and Najm Al-Deen N., Abdo and Mubarak H. G., Abdo and Talep S., Mohammed and Ameen A., Hasan and Ali N., Abdo and Sherzod, Turaev and Yusoff, Nukman (2023) An automatic visual inspection of oil tanks exterior surface using unmanned aerial vehicle with image processing and cascading fuzzy logic algorithms. Drones, 7 (2). pp. 1-25. ISSN 2504-446X

	PDF (Journal) - Published Version Restricted to Repository staff only Download (11MB) | Request a copy
Preview	PDF (Scopus) - Supplemental Material Download (352kB) | Preview

Abstract

This paper presents an automatic visual inspection of exterior surface defects of oil tanks using unmanned aerial vehicles (UAVs) and image processing with two cascading fuzzy logic algorithms. Corrosion is one of the defects that has a serious effect on the safety of the surface of oil and gas tanks. At present, human inspection, and climbing robots inspection are the dominant approach for rust detection in oil and gas tanks. However, there are many shortcomings to this approach, such as taking longer, high cost, and covering less surface area inspection of the tank. The purpose of this research is to detect the rust in oil tanks by localizing visual inspection technology using UAVs, as well as to develop algorithms to distinguish between defects and noise. The study focuses on two basic aspects of oil tank inspection through the images captured by the UAV, namely, the detection of defects and the distinction between defects and noise. For the former, an image processing algorithm was developed to improve or remove noise, adjust the brightness of the captured image, and extract features to identify defects in oil tanks. Meanwhile, for the latter aspect, a cascading fuzzy logic algorithm and threshold algorithm were developed to distinguish between defects and noise levels and reduce their impact through three stages of processing: The first stage of fuzzy logic aims to distinguish between defects and low noise generated by the appearance of objects on the surface of the tank, such as trees or stairs, and reduce their impact. The second stage aims to distinguish between defects and medium noise generated by shadows or the presence of small objects on the surface of the tank and reduce their impact. The third stage of the thresholding algorithm aims to distinguish between defects and high noise generated by sedimentation on the surface of the tank and reduce its impact. The samples were classified based on the output of the third stage of the threshold process into defective or non-defective samples. The proposed algorithms were tested on 180 samples and the results show its superiority in the inspection and detection of defects with an accuracy of 83%.

Item Type:	Article (Journal)
Uncontrolled Keywords:	oil tank; automatic visual inspection; unmanned aerial vehicle; camera; fuzzy logic
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Rawad Abdulghafor
Date Deposited:	08 Mar 2023 11:55
Last Modified:	21 Jun 2023 09:20
URI:	http://irep.iium.edu.my/id/eprint/103862

Actions (login required)

View Item