Automatic Extraction of Material Defect Size by Infrared Image Sequence

Yuan, Lihua and Zhu, Xiao and Sun, Quanbin and Liu, Haibo and Yuen, Peter and Liu, Yonghuai (2020) Automatic Extraction of Material Defect Size by Infrared Image Sequence. Applied Sciences, 10 (22). p. 8248. ISSN 2076-3417

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Abstract

A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame from the sequence is an important problem to be solved first. A maximum standard deviation of the sensitive region method was proposed, which can identify a reasonable image frame automatically from an infrared image sequence; then, a stratagem of image composition was applied for enhancing the detection of deep defects in the key frame. Blob analysis had been adopted to acquire general information of defects such as their distributions and total number of defects. A region of interest of the defect was automatically located by its key frame combined with blob analysis. The defect information was obtained through image segmentation techniques. To obtain a robustness of results, a method of two steps of detection was proposed. The specimen of polyvinyl chloride with two artificial defects at different depths as an example was used to demonstrate how to operate the proposed method for an accurate result. At last, the proposed method was successfully adopted to examine the damage of carbon fiber-reinforced polymer. A comparative study between the proposed method and several state-of-the-art ones shows that the former is accurate and reliable and may provide a more useful and reliable tool for quality assurance in the industrial and manufacturing sectors.

Item Type: Article
Identification Number: https://doi.org/10.3390/app10228248
Dates:
DateEvent
17 November 2020Accepted
20 November 2020Published Online
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Quanbin Sun
Date Deposited: 21 Dec 2021 16:12
Last Modified: 21 Dec 2021 16:12
URI: http://www.open-access.bcu.ac.uk/id/eprint/12554

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