# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 1 Introduction
Meanwhile, on the basis of keeping the size of the defect area unchanged, morphological opening operation that smoothens the boundary of the defect and eliminates small spikes has been reported. [PERSON] [16] applied the Levenberg-Marquardt iterative algorithm for denoising, thereby improving the contrast of the thermal data. More recent work has been using bionic intelligence algorithms for denoising and then the edge detection of defect features through the ant colony algorithm [17] after the time derivative profile is polynomial fitted. A more advanced hybrid method that integrates the simulation annealing algorithm (SA) and Nelder-Mead simplex search method (NM) for the quantification of the sizes and depths of subsurface defects of the carbon fiber-reinforced plastic (CFRP) laminate materials has also been attempted with good results [18]. [PERSON] [19] showed that the error for defining the defect boundaries followed the finite spatial resolution of the numerical model in the [PERSON]'s segmentation method is rather significant. [PERSON] [20] adopted the watershed technique and Gabor filtering to detect defects, and good results have been obtained. Furthermore, there are a number of image segmentation techniques available for the quantitative recognition of defects, e.g., the iterative threshold method, the maximum entropy method, the minimum error method, the Otsu method, and the minimum deviation method; however, none of them is capable of extracting defect features automatically without the need for operator intervention. Although the Otsu method has been shown to be more capable of extracting defects even for those in different depths [21], it still cannot realize the extraction of defect features automatically. Feature extraction is another critical step in the image based on NDE for defect detection. There are two main approaches that have been used for the quantitative detection of defect size: (i) the analysis of the surface temperature field data and (ii) through the digital processing of the image data. One of the most classical ways for the quantitative assessment of defect dimensions and the extraction of the defect temperature profile is using the Wetsel and McDonald's temperature field analysis method through the full width at half maxima (FWHM) of the defect feature [22]. This is still a popular method being used for defects characterization [23,24]. [PERSON] [25] improved the method by using the time derivative of the surface temperature distribution to determine the defect size through the half-height tangent method. [PERSON] [26] demonstrated the use of the signal to noise ratio (SNR) analysis of the temperature distribution profile for the assessment of physical property of defects: when the SNR of the observed thermal anomaly is larger than 15, then the feature is considered as induced by defects. In the second approach of defects analysis which focuses more on digital processing of infrared images, a sparse principal component thermography (SPCT) method had been proposed to enhance the contrast of the defect induced thermal anomaly with respect to the background and the noise of the imaging sensor [27]. At present, the most mainstream method of defect representation (area) is still using the equivalent pixel ratio method [5,6], which estimates the defect area through the number of image pixels of the defect image. The equivalent length ratio for each pixel can be calibrated by using the known dimension of the specimen. Based on the outstanding issues as outlined above, this work attempts to address the automatic selection of suitable image frames from an infrared image sequence for processing and to characterize the defects of the material robustly and accurately under the long pulse thermographic experimental configuration. The workability of the proposed method is demonstrated by the detection of artificial defects in polyvinyl chloride (PVC), and the feasibility and accuracy of this method are verified by the impact damage detection of carbon fiber-reinforced plastics (CFRP).