# Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
## 1 Introduction  
The importance of non-destructive testing (NDT) in industry occupies a large part of the inspection of machines or structures in various fields such as shipbuilding, automobiles, construction, civil engineering, and nuclear power [1, 2, 3]. Structural steel used for these elements must maintain the strength range required, but the presence of defects causes changes in local mechanical properties and has a fatal impact on stability and lifespan [4, 5]. Therefore, it is necessary to inspect for defects in order to reduce effects such as fractures.  
Stainless steel (STS) has excellent formability, corrosion resistance and heat resistance. In particular, austenitic stainless steel has high mechanical properties and corrosion resistance; therefore, it can be applied in a wide temperature range and various operating conditions, from cryogenic to high temperature [6, 7, 8]. In general, it is used in various fields such as interior and exterior materials for construction, automobile parts, aircraft fuselages, and medical devices. However, defects can occur during manufacturing and maintenance steps [9]. Therefore, NDT techniques for detecting defects present on the surface or inside are very important.  
A variety of NDT studies have been performed, such as eddy current testing [10, 11, 12, 13], ultrasonic [14, 15], acoustic emission [16, 17], radiographic [18], liquid penetrant testing [9], magnetic-particle inspection [19, 20], and infrared thermography. Magnetic particle inspection, liquid penetrate test, and ultrasonic have typically used as NDT inspection techniquesapplied to STS material inspection. Recently, infrared thermography (IRT) techniques capable of quantitative evaluation have been applied in many fields.  
IRT for non-destructive testing and evaluation (NDT&E) is mainly applied to detect subsurface features (backside defect, anomalies, etc.) [5; 21; 22]. IRT is a technology that analyzes and acquires thermal response signals using a non-contact thermal imaging device [23]. IRT detects radiation energy emitted from objects in the infrared range of the electromagnetic spectrum. The infrared range corresponds to wavelengths longer than the visible portion of the spectrum. Therefore, IRT can be utilized to detect defects inside the surface of metal plates. Based on these characteristics of IRT, it is possible to effectively detect a backside defect.  
In this study, lock-in infrared thermography, one of the active IRT techniques, was applied to automatically detect defects on the backside of STS304 metal plates. For clear defect detection, binarization processing using grayscale-based histograms and first, second, de-noising were performed. Automatic defect detection was performed based on the threshold value using the metric roundness equation. Finally, a detectability analysis according to filtering was performed.  
The reminder of the paper is composed as follows. In Section 2, The theory of lock-in signal processing and the Otsu algorithm is provided. Section 3 describes the information of STS304 and experimental process. Section 4 presents experimental data, and Section 5 concludes the paper.