## 1 Introduction  
The working temperature of a gas turbine is rising continuously due to the continuous pursuit of performance. A common way to deal with this situation is to spray Thermal Barrier Coatings (TBCs) on the turbine blades, so that the components can bear a large temperature gradient when exposed to heat flow [1, 2]. During long term service, cracks are one of the most common and serious defects in TBCs [3]. Since TBC is a multilayer structure including top-coating layer, transition layer and the substrate, under high temperature and harsh working conditions, the thermodynamic performance of different layers become inconsistent, especially though a mismatch of thermal expansion coefficients. Such mismatch between the layers will induce residual stress, which is considered to be the major cause of the cracks [4]. The thermal insulation performance of the TBC will significantly be reduced by a large sized crack or the penetration crack. When growing to a certain extent, a crack will cause the coating to reach spallation, which makes TBC totally fail and thus creates serious security problems. Therefore, implementing TBC crack detection is of great importance. The main crack detection methods include manual detection, ultrasonic detection, laser scanning detection, infrared image-based detection, etc. [5]. Traditional manual detection requires professional staff to shut down the gas turbine regularly and observe whether the TBCs have cracks. This approach has many disadvantages such as being expensive, subjective, and inefficient. Alternatives such as ultrasonic detection, laser scanning detection and infrared image-based detection are regarded as non-destructive evaluation methods [6; 7]. However, due to the limitations of the working environment, ultrasonic and laser detection is difficult to use for on-line monitoring. In contrast, infrared image detection has relatively low requirements for the working environment [8; 9]. In the neighborhood of 1500 \({}^{\circ}\)C, the infrared thermal imager only needs a heat shield to run successfully. Hence, studies of infrared detection technology have garnered increasing attention [10; 11; 12; 13]. Infrared image processing methods for detection of coating defects usually have four steps: image pre-process, image segmentation, feature extraction, and classifier construction [14]. Image pre-procession methods are mostly for improving image quality by image de-noising and enhancement [15; 16]. Image segmentation methods collect information and characteristics from the image, and divide the image into Regions of Interest (ROIs) and background. Researchers usually start ROIs extraction by either applying a kind of designated threshold, or edge detection by analyzing the local gradients [17; 18]. Feature extraction and classification methods of images are also extensively studied [19; 20; 21; 22]. However, traditional image processing methods are used for surface cracks detection. It is generally agreed that stress accumulation during service will form several micro-cracks at the interface, and then expand to form a dominant interface crack, which may grow vertically to the surface and eventually cause spallation [4; 23]. In addition, the growth of thermally grown oxide will also bring residual stress, resulting in horizontal cracks and spallation in the coating [24; 25]. Therefore, it is possible to have both surface cracks and internal cracks in the TBCs. However, the characteristics of surface cracks and internal cracks are very different. Surface cracks generally have a certain linear feature while the features of internal cracks are very fuzzy. Moreover, there are plenty of pores in the coating. The size and shape of a small internal crack is so close to the pore, that it will show a very similar effect on the temperature distribution. Such pores will bring many false alarm points in internal crack detection and thus make traditional image processing methods less accurate.