## 1 Introduction
Therefore, the non-uniform temperature distribution was solved by applying the Fast Fourier Transform (FFT) algorithm based on Gaussian filtering [12]. For automated defect detection of moving inspection objects, the you only look once (YOLO) technique is the most efficient. YOLOv4 can classify and detect in real time and can predict at a faster speed than Faster RCNN. Big data is required to improve the accuracy \begin{table}
\begin{tabular}{c c c c} \hline \hline
**NDT Technique** & **Capability** & **Limitation** & **Cost** \\ \hline \multirow{2}{*}{VT} & \multirow{2}{*}{Simple surface monitoring} & Limited to surface defects; & \multirow{2}{*}{Low} \\  & & experience dependent & \\ \hline \multirow{2}{*}{PT} & \multirow{2}{*}{Suitable for mass production} & Sensitive to environment; & \\  & & surface only & \\ \hline \multirow{2}{*}{MPT} & \multirow{2}{*}{Effective for ferromagnetic parts} & Surface only; & \multirow{2}{*}{Medium} \\  & & flat, uncoated surface required & \\ \hline \multirow{2}{*}{IRT} & \multirow{2}{*}{Non-contact, fast surface inspection} & Limited depth detection; & \multirow{2}{*}{High} \\  & & sensitive to environmental conditions & \\ \hline \multirow{2}{*}{UT} & \multirow{2}{*}{Surface and internal inspection} & Manual; & \multirow{2}{*}{High} \\  & & skill dependent; & \\  & & expensive equipment & \\ \hline \multirow{2}{*}{RT} & \multirow{2}{*}{Surface and internal inspection} & Expensive; & \multirow{2}{*}{Very high} \\  & & time consuming; & \\ \multicolumn{2}{c}{hazardous} & & \\ \hline AE & Surface and internal analysis & Stress wave attenuation issues & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of characteristics for each NDT technology [5; 6; 7]. of detecting cracks in thermal images. Therefore, data augmentation is required to secure big data. In the era of the fourth industrial revolution, AI is an essential technology, and various AI deep learning technologies are being applied for image processing. The possibility of applying various AI technologies (not just YOLO) to the IRT field is being reviewed. [PERSON] et al. [13] presented an improved lightweight MobileNetV2_DeepLabV3+ algorithm for dam crack detection, utilizing a dataset of 1560 images annotated with the \"LabelMe\" tool and depth cameras. Enhancements to the ASPP module and a multifeature fusion structure improved segmentation accuracy, achieving better performance compared to existing methods like U-Net and PSPNet while significantly reducing computational costs and training time, making it suitable for drone-based automatic crack detection. [PERSON] et al. [14] proposed an improved lightweight YOLOv5s model for real-time detection of pitaya fruits in field environments, integrating ShuffleNetV2, BiFPN, SE attention modules, and simOTA for enhanced feature extraction, efficient multistage fusion, and improved occlusion handling, achieving a compact model with high precision. [PERSON] et al. [15] presented detailed analyses of Kirkuk City using Sentinel-2 data, GIS, and Envi-based techniques, creating true color, false color, and highlight-optimized maps to visualize natural landscapes, vegetation health, land cover, and urban areas. The core contributions of this study are as follows. First, the non-uniform heat distribution caused by foreign substances was solved using the Gaussian-filtering-based FFT algorithm. Second, in order to secure big data for AI deep learning, a correlation equation based on regression analysis was derived to obtain virtual data. Finally, a process for detecting cracks on moving objects using YOLOv4 was presented.