Article an automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images
Utilising cooling stimulation as a thermal excitation means has demonstrated profound capabilities of detecting sub-surface metal loss using thermography. Previously, a prototype mechanism was introduced which accommodates a thermal camera and cooling source and operates in a reciprocating motion sc...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
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MDPI
2021
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| Online Access: | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90925 |
| _version_ | 1848765462387621888 |
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| author | Doshvarpassand, Siavash Wang, Xiangyu |
| author_facet | Doshvarpassand, Siavash Wang, Xiangyu |
| author_sort | Doshvarpassand, Siavash |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Utilising cooling stimulation as a thermal excitation means has demonstrated profound capabilities of detecting sub-surface metal loss using thermography. Previously, a prototype mechanism was introduced which accommodates a thermal camera and cooling source and operates in a reciprocating motion scanning the test piece while cold stimulation is in operation. Immediately after that, the camera registers the thermal evolution. However, thermal reflections, non-uniform stimulation and lateral heat diffusions will remain as undesirable phenomena preventing the effective observation of sub-surface defects. This becomes more challenging when there is no prior knowledge of the non-defective area in order to effectively distinguish between defective and non-defective areas. In this work, the previously automated acquisition and processing pipeline is re-designed and optimised for two purposes: 1—Through the previous work, the mentioned pipeline was used to analyse a specific area of the test piece surface in order to reconstruct the reference area and identify defects. In order to expand the application of this device over the entire test area, re-gardless of its extension, the pipeline is improved in which the final surface image is reconstructed by taking into account multiple segments of the test surface. The previously introduced pre-pro-cessing method of Dynamic Reference Reconstruction (DRR) is enhanced by using a more rigorous thresholding procedure. Principal Component Analysis (PCA) is then used in order for feature (DRR images) reduction. 2—The results of PCA on multiple segment images of the test surface revealed different ranges of intensities across each segment image. This potentially could cause mistaken interpretation of the defective and non-defective areas. An automated segmentation method based on Gaussian Mixture Model (GMM) is used to assist the expert user in more effective detection of the defective areas when the non-defective areas are uniformly characterised as background. The final results of GMM have shown not only the capability of accurately detecting subsurface metal loss as low as 37.5% but also the successful detection of defects that were either unidentifiable or invisible in either the original thermal images or their PCA transformed results. |
| first_indexed | 2025-11-14T11:35:38Z |
| format | Journal Article |
| id | curtin-20.500.11937-90925 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:38Z |
| publishDate | 2021 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-909252023-05-08T07:40:32Z Article an automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images Doshvarpassand, Siavash Wang, Xiangyu Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering cold-active infrared thermography non-destructive testing metal loss defect detection Image processing structural health monitoring vision-based sensors ADAPTIVE HISTOGRAM EQUALIZATION CONTRAST ENHANCEMENT Image processing cold-active infrared thermography metal loss defect detection non-destructive testing structural health monitoring vision-based sensors Motion Principal Component Analysis Respiration Thermography Thermography Respiration Principal Component Analysis Motion Utilising cooling stimulation as a thermal excitation means has demonstrated profound capabilities of detecting sub-surface metal loss using thermography. Previously, a prototype mechanism was introduced which accommodates a thermal camera and cooling source and operates in a reciprocating motion scanning the test piece while cold stimulation is in operation. Immediately after that, the camera registers the thermal evolution. However, thermal reflections, non-uniform stimulation and lateral heat diffusions will remain as undesirable phenomena preventing the effective observation of sub-surface defects. This becomes more challenging when there is no prior knowledge of the non-defective area in order to effectively distinguish between defective and non-defective areas. In this work, the previously automated acquisition and processing pipeline is re-designed and optimised for two purposes: 1—Through the previous work, the mentioned pipeline was used to analyse a specific area of the test piece surface in order to reconstruct the reference area and identify defects. In order to expand the application of this device over the entire test area, re-gardless of its extension, the pipeline is improved in which the final surface image is reconstructed by taking into account multiple segments of the test surface. The previously introduced pre-pro-cessing method of Dynamic Reference Reconstruction (DRR) is enhanced by using a more rigorous thresholding procedure. Principal Component Analysis (PCA) is then used in order for feature (DRR images) reduction. 2—The results of PCA on multiple segment images of the test surface revealed different ranges of intensities across each segment image. This potentially could cause mistaken interpretation of the defective and non-defective areas. An automated segmentation method based on Gaussian Mixture Model (GMM) is used to assist the expert user in more effective detection of the defective areas when the non-defective areas are uniformly characterised as background. The final results of GMM have shown not only the capability of accurately detecting subsurface metal loss as low as 37.5% but also the successful detection of defects that were either unidentifiable or invisible in either the original thermal images or their PCA transformed results. 2021 Journal Article http://hdl.handle.net/20.500.11937/90925 10.3390/s21144811 English http://purl.org/au-research/grants/arc/LP180100222 http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering cold-active infrared thermography non-destructive testing metal loss defect detection Image processing structural health monitoring vision-based sensors ADAPTIVE HISTOGRAM EQUALIZATION CONTRAST ENHANCEMENT Image processing cold-active infrared thermography metal loss defect detection non-destructive testing structural health monitoring vision-based sensors Motion Principal Component Analysis Respiration Thermography Thermography Respiration Principal Component Analysis Motion Doshvarpassand, Siavash Wang, Xiangyu Article an automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images |
| title | Article an automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images |
| title_full | Article an automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images |
| title_fullStr | Article an automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images |
| title_full_unstemmed | Article an automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images |
| title_short | Article an automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images |
| title_sort | article an automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images |
| topic | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering cold-active infrared thermography non-destructive testing metal loss defect detection Image processing structural health monitoring vision-based sensors ADAPTIVE HISTOGRAM EQUALIZATION CONTRAST ENHANCEMENT Image processing cold-active infrared thermography metal loss defect detection non-destructive testing structural health monitoring vision-based sensors Motion Principal Component Analysis Respiration Thermography Thermography Respiration Principal Component Analysis Motion |
| url | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90925 |