Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application.

active infrared thermography aerospace components composites unmanned aerial vehicle (UAV) video stabilization

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
25 Feb 2021
Historique:
received: 23 12 2020
revised: 03 02 2021
accepted: 15 02 2021
entrez: 6 3 2021
pubmed: 7 3 2021
medline: 7 3 2021
Statut: epublish

Résumé

Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV's unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV's unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.

Identifiants

pubmed: 33668881
pii: s21051604
doi: 10.3390/s21051604
pmc: PMC7956756
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Innovate UK
ID : 105625

Références

IEEE Trans Pattern Anal Mach Intell. 2006 Jul;28(7):1150-63
pubmed: 16792103
Sensors (Basel). 2017 Dec 26;18(1):
pubmed: 29278361

Auteurs

Shashank Pant (S)

National Research Council Canada, Ottawa, Ontario, K1A 0R6, Canada.

Parham Nooralishahi (P)

Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada.

Nicolas P Avdelidis (NP)

Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada.
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.

Clemente Ibarra-Castanedo (C)

Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada.

Marc Genest (M)

National Research Council Canada, Ottawa, Ontario, K1A 0R6, Canada.

Shakeb Deane (S)

School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.

Julio J Valdes (JJ)

National Research Council Canada, Ottawa, Ontario, K1A 0R6, Canada.

Argyrios Zolotas (A)

School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.

Xavier P V Maldague (XPV)

Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada.

Classifications MeSH