The Application of Machine Learning to Paint Condition Assessment Using Hyperspectral Data

Ayoub Alayoub, Samer Abed El Rahim, Samir Mustapha, Darine Salam, Ali Tehrani, Nguyen Lu Dang Khoa

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)

Abstract

Paint provides a shield that protects our structures, particularly steel structures, from corrosion and damage due to harsh environmental conditions such as sunlight and humidity. It is essential to ensure that paint coating works effectively and remains in good structural conditions (without peeling or fracture). Assessing the paint condition is complex, especially on large structures such as bridges, and can be time-consuming and costly. The traditional visual inspection methods depend on the experience and evaluation of inspectors in the field and require hard labor and time to perform. This study aims to employ hyperspectral imaging and image classification approaches to develop a structural condition monitoring system that rapidly assesses paint conditions and degradation. Several classification algorithms, such as Decision Tree, Support Vector Machines, Logistic Regression, and Naïve Bayes, were trained and tested. The results obtained showed that the Decision Tree classifier outperformed the rest of the classifiers, achieving a highly accurate assessment of the paint condition and degradation levels with a detection accuracy of 0.98.

Original languageEnglish
Title of host publication2022 12th Workshop on Hyperspectral Imaging and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2022
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-6654-7069-8
ISBN (Print)978-1-6654-7070-4
DOIs
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventWorkshop on Hyperspectral Image and Signal Processing - Rome, Italy
Duration: 13 Sept 202216 Sept 2022
Conference number: 12

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2022-September
ISSN (Electronic)2158-6276

Workshop

WorkshopWorkshop on Hyperspectral Image and Signal Processing
Abbreviated titleWHISPERS
Country/TerritoryItaly
CityRome
Period13/09/202216/09/2022

Keywords

  • classification algorithm
  • hyperspectral imaging
  • machine learning
  • Paint condition assessment

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