# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 1 Introduction  
The health monitoring of materials and structures plays a vital role in the fields of aerospace, industry, medical, and construction [1, 2]. In many major projects, even minor material defects may cause major hidden danger. Therefore, nondestructive testing (NDT) technology provides a strong technical guarantee for the safety of material structure. As a new nondestructive testing technology, pulsed thermal nondestructive testing (TNDT) is widely used in the detection of defects in materials and structures due to its large detection area, fast detection speed, detection convenience, and cheap equipment [3]. Researchers can obtain the continuous change process of the transient temperature field on the material surface through the infrared thermal imager. A sequence of infrared images expresses the evolution of temperature intuitively. The method of digital image processing is applied to assessand analyze the information of the defect quantitatively such as the location, shape, and size of the defect. To quantify the size of defects has been one of the most critical processes in the infrared-based nondestructive defect detection. It is the deterministic factor for the acceptance or rejection of the specimen during the quality control process. Despite the popularity for using the thermographic technique as an efficient method for defect detection, there are still a number of outstanding issues that are under active research. First of all, an inspector needs to select the frame that best represents the material defect information from hundreds of infrared images. There are rare research studies on the qualitative index of frame number selection at present. Generally, this had been proceeded through the identification of the image frames that exhibited substantial surface temperature contrast between the defect area and the sound region [4]. There is an element of subjectivity in these approaches. [PERSON] et al. [5] pointed out that the quantification of the defect size was critically dependent on the selection of the appropriate time frame of the thermogram for defect evaluations. It was concluded that in order to obtain a more accurate dimensions of the defect, it was necessary to analyze the infrared thermal image with the largest temperature difference contrast or immediately thereafter by analyzing the defect results at different distances and inspection times. [PERSON] et al. [6] selected the frames that depicted the largest average surface temperature of the specimen as the candidate image frame data. However, the robustness of the image frame sequence selection methodology has been shown to be generally poor, and the proposed method of selecting an infrared sequence key frame has poor accuracy in the final defect extraction. In the next place, raw images are usually not suitable for accurate material assessment [7]. In order to further obtain the defect information of the material damage, the image needs to be digitally processed. However, the contrast of infrared images is commonly low and normally accompanied with significant blurred edges, especially those associated with structural defects such as cracks [8; 9]. A number of factors such as uneven heating, precision of the equipment, electro-magnetic radiation (emission) from the background etc. are the main contributions that reduce the contrast of the thermogram significantly [10; 11]. Undoubtedly, this is an additional challenge to the qualitative and quantitative identification and evaluation of defects especially when the thermographic technique is employed for the NDE. There have been two main ways for processing infrared images: the non-uniformity correction method [12; 13; 14] and the detailed enhancements of infrared images. [PERSON] [15] pointed out that the median filter had better edge retention characteristics on the basis of noise removal capability particularly when imagery contains the edge feature of the defects.