Automatic Extraction of Material Defect Size by Infrared Image Sequence
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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/22/8248 |
| _version_ | 1797547249851432960 |
|---|---|
| author | Lihua Yuan Xiao Zhu Quanbin Sun Haibo Liu Peter Yuen Yonghuai Liu |
| author_facet | Lihua Yuan Xiao Zhu Quanbin Sun Haibo Liu Peter Yuen Yonghuai Liu |
| author_sort | Lihua Yuan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:41:37Z |
| format | Article |
| id | doaj.art-105e428764bd49ea8b9d5aefaeac117c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:41:37Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-105e428764bd49ea8b9d5aefaeac117c2023-11-20T21:44:36ZengMDPI AGApplied Sciences2076-34172020-11-011022824810.3390/app10228248Automatic Extraction of Material Defect Size by Infrared Image SequenceLihua Yuan0Xiao Zhu1Quanbin Sun2Haibo Liu3Peter Yuen4Yonghuai Liu5Key Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hangkong University, Nanchang 330063, ChinaKey Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hangkong University, Nanchang 330063, ChinaDepartment of Computer Science, Birmingham City University, Birmingham B5 5JU, UKSchool of Computer Science and Technology, Harbin Engineering University, Harbin 150001, ChinaDefence Academy of United Kingdom, Cranfield University, Shrivenham, Swindon SN6 8LA, UKDepartment of Computer Science, Edge Hill University, Ormskirk L39 4QP, UKA 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.https://www.mdpi.com/2076-3417/10/22/8248long pulse thermographyinfrared image sequenceblob analysisregion of intereststandard deviation |
| spellingShingle | Lihua Yuan Xiao Zhu Quanbin Sun Haibo Liu Peter Yuen Yonghuai Liu Automatic Extraction of Material Defect Size by Infrared Image Sequence Applied Sciences long pulse thermography infrared image sequence blob analysis region of interest standard deviation |
| title | Automatic Extraction of Material Defect Size by Infrared Image Sequence |
| title_full | Automatic Extraction of Material Defect Size by Infrared Image Sequence |
| title_fullStr | Automatic Extraction of Material Defect Size by Infrared Image Sequence |
| title_full_unstemmed | Automatic Extraction of Material Defect Size by Infrared Image Sequence |
| title_short | Automatic Extraction of Material Defect Size by Infrared Image Sequence |
| title_sort | automatic extraction of material defect size by infrared image sequence |
| topic | long pulse thermography infrared image sequence blob analysis region of interest standard deviation |
| url | https://www.mdpi.com/2076-3417/10/22/8248 |
| work_keys_str_mv | AT lihuayuan automaticextractionofmaterialdefectsizebyinfraredimagesequence AT xiaozhu automaticextractionofmaterialdefectsizebyinfraredimagesequence AT quanbinsun automaticextractionofmaterialdefectsizebyinfraredimagesequence AT haiboliu automaticextractionofmaterialdefectsizebyinfraredimagesequence AT peteryuen automaticextractionofmaterialdefectsizebyinfraredimagesequence AT yonghuailiu automaticextractionofmaterialdefectsizebyinfraredimagesequence |