An Automated Pipeline for Dynamic Detection of Sub-Surface Metal Loss Defects across Cold Thermography Images.

Siavash Doshvarpassand,Xiangyu Wang

doi:10.3390/s21144811

Siavash Doshvarpassand, Xiangyu Wang

Open Access

https://doi.org/10.3390/s21144811

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Abstract

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, regardless 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-processing 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.

Highlights

Corrosion is generally defined as the deterioration of the surface and internal microstructure in most metals when a reaction with a corrosive environment occurs [1,2,3,4,5,6,7]
Python programming language was used for scripting and prototyping
Those include: (1), the acquisition of video signals captured by a thermal camera; (2) decomposing the video to frames, trimming each frame to only reveal the test piece exposed surface to the cold burst and stacking all frames in order to produce a 3D array of frames over the exposure time; (3) applying Dynamic Reference Reconstruction (DRR) by mapping a block of pixels, generating the contrastenhanced images by applying Adaptive Histogram Equalisation (AHE) and filtering the enhanced image based on a multi-boundary condition on block location; (4) applying Principal Component Analysis (PCA) on equal size segments of DRR images by reconstructing the PCA input matrix; and (5) applying an iterative “GrabCut” algorithm on first and second principal components of each segment

Summary

Introduction

Corrosion is generally defined as the deterioration of the surface and internal microstructure in most metals when a reaction with a corrosive environment occurs [1,2,3,4,5,6,7]. General corrosion detection is usually conducted in the form of determining the loss of material as the effect of corrosion economy and subsequent operations [1,8,9,10,11]. General corrosion detection is usually co ducted in the form of determining the loss of material as the effect of corrosion activiti. This yields a common yet crucial activity in corrosion mitigation, manage2mofe2n2t and ri based inspections [1,6,12,13,14]. Temperature is one of the most common identifications of the equipment and co aanctp(divorinitsieekn0s-b.tK’aTss)hehirdsaeyadinilietsahlptdeesccoetainlocednocsimttr[iom1om,n6o,na1[1g2y1–ne1,te14tc5]ir.–cu1cr7iaa]d.l iaAactltiliovonitb.yjeMinctecsnoatritrooansietoednmrmapidetirigaaattituoiornen,ismcaalnabasogsviefemieaedbntsaosluintferazre baTnedmwpiedrtahtuareloins gonteheofetlheectmroomst acgomnemtiocnsipdeecnttriufimcati(ownasvoefltehnegetqhuiinpmthenetraanndgceomof- 0.75–10 poμnmen)t.’sInhferaalrthedcothnderitmioong[r1a1p,1h5y–1, 7th].eArmll aolbijmectasgaint ag toerm, ipnergaetnuereraTl,atbhoevremaobgsroaluptheyzeisroconsider (Ta>n 0NKD)Erapdriaatcetieclectthroamt aalglonewtisc urasdtioatpioenr.ceMivenethioenaetdwradvieastion iasncloabssjeificet’ds assuirnfafrcaere[d18]

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