###### Abstract  
In general, when using infrared thermography (IRT) techniques to excite a heat source on the surface of an inspection object, the heat source is focused on the center of the image of the infrared (IR) camera. If the object to be inspected is small, uniform excitation of the heat source is possible, but if the area is large, the heat source is concentrated locally, resulting in uneven heat distribution. Therefore, in this study, heat distribution was analyzed after inducing a non-uniform heat source by exciting the heat source at different locations. Additionally, the fast Fourier transform (FFT) algorithm with Gaussian filtering was applied to resolve the non-uniform distribution of the heat sources. Excellent results were obtained from the amplitude image, and the effectiveness of the FFT algorithm was verified using the Otsu algorithm. Finally, the signal-to-noise ratio (SNR) was calculated, and the detection ability according to each thinning rate was analyzed. optical thermography; fast Fourier transform; Gaussian filtering; thermal equalization; validation evaluation +
Footnote †: journal: Medical Imaging  
0000-0001-8978-2277-0000][PERSON]  
0000-0001-8978-2277-0000][PERSON]  
0000-0002-313-22223  
The active IRT technique is a technique that uses an external heat source to provide heat to the inspection object and then detects the emitted radiant energy with an infrared (IR) camera [12; 13]. External heat sources such as optical, electromagnetic, vibration, and microwave can be selected appropriately depending on the inspection object and surrounding environment. The area of the CLP of nuclear power plants in Korea is generally too large, and the UT technique, which is used most widely in Korea, has very high accuracy, but it takes at least two months to inspect even one power plant. Therefore, in this study, thinning defect evaluation was performed using a halogen lamp, one of the optical techniques. In recent years, IRT technology has made great progress by applying artificial intelligence (AI). [PERSON] et al. proposed a deep convolutional autoencoder thermography (DCAT) data analysis technique [14]. The original data were restored using the proposed technique to reduce noise and uneven background. In addition, by using a deep autoencoder, non-linear features can be extracted to obtain information about the internal structure and defects of a modern artwork that is the object of inspection. [PERSON] et al. proposed a technique that converts the 2D images acquired using IRT techniques into 3D images, enabling the visualization of defects that exist inside the inspection object and providing location information on the actual defects [15]. [PERSON] et al. proposed a deep autoencoder thermography (DAT) technique to detect surface defects in composite materials [16]. Information can be acquired from the non-linear temperature profile of the multi-layer structure, and defect information is provided by visualizing hidden layers. When inspecting an object with a large surface area, such as a CLP, using a halogen lamp [17], the inspection is performed without knowing the location of the internal defect. In this case, the heat is provided intensively from the surface due to the lamp, rising locally. If relatively little heat is generated at the location of the internal defect, detectability is affected. Therefore, to solve this problem, this study presents a thermal equalization process by applying Gaussian filtering to the FFT algorithm. Generally, when a halogen lamp is used, the heat source is excited in the form of a sine wave, and phase and amplitude data are acquired using the four-point algorithm of the lock-in thermography (LIT) technique. However, when a non-uniform heat source is provided, the detectability of the phase and amplitude data is reduced, thereby reducing reliability. Therefore, in this study, phase and amplitude data were acquired after applying thermal equalization to the raw image using the FFT algorithm.