An Automatic Visual Inspection Method based on Statistical Approach for Defect Detection of Ship Hull Surfaces

A. Jalalian*, W.F. Lu, F.S. Wong, S.M. Ahmed, C.-M. Chew

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

Robotized blasting of ship hull surfaces requires an accurate identification of defective regions of the hull to maximize the blasting efficiency. Accurate surface defect detection may not be achieved by current manual procedures, as its success is highly vulnerable to the human operators' experience and their subjective judgements. Therefore, there is a need for a more accurate and non-subjective method for defect detection. This paper proposes a computer vision based method for detection of ship hull defects. The method utilizes the histogram of hue and entropy data of the hue to identify the defects in two steps. Step 1 is an automatic circular thresholding based on the histogram of hue to distinguish the defects whose hue is different from the defect-free regions. A wrapped Gaussian mixture model is utilized to estimate the circular hue histograms, and maximum likelihood criterion is adopted to set the thresholds. Step 2 uses the probability distribution of the entropy for each segment identified in the first step to decide whether the segments are either defective, defect-free or a mixture of both. For the mixed regions, a Gaussian mixture model is fitted to the probability distribution of the entropy. The maximum likelihood criterion is utilized to segment these regions so as to discriminate their defective and defect-free parts. The high accuracy (F-measure=0.89) and short execution time (3.5 s) of the proposed method show that it is a good starting point for an automatic defect detection for a fully autonomous ship hull blasting.

Original languageEnglish
Title of host publication2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
Place of PublicationPiscataway, NJ
PublisherIEEE Computer Society Press
Pages445-450
Number of pages6
ISBN (Electronic)978-1-5386-3593-3, 978-1-5386-2514-9
ISBN (Print)978-1-5386-3594-0
DOIs
Publication statusPublished - 4 Dec 2018
Externally publishedYes
Event14th IEEE International Conference on Automation Science and Engineering, CASE 2018 - Technical University of Munich, Campus Garching, Munich, Germany
Duration: 20 Aug 201824 Aug 2018
Conference number: 14

Publication series

NameIEEE International Conference on Automation Science and Engineering
PublisherIEEE
Volume2018
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference14th IEEE International Conference on Automation Science and Engineering, CASE 2018
Abbreviated titleCASE 2018
CountryGermany
CityMunich
Period20/08/1824/08/18

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