## 1 Introduction
studied the automatic classification of different defects in TFM images, used different machine learning models to reconstruct the images of pipeline crack defects and pore defects, proved the superiority of the hog poly SVM algorithm, and proposed a TFM imaging method for small targets of buried pipelines [29]. [PERSON] et al. proposed an improved adaptive window genetic algorithm gcForest (AWGA-gcForest), which is based on FMC data processing, eliminates the interference of incident wave and a back wall echo, reduces the computational complexity of the gcForest algorithm, and improves the classification accuracy [30]. [PERSON] et al. proposed an automatic interpretation method of ultrasonic data, tested artificial defects with different characteristics in carbon-fiber-reinforced polymer composites (CFRP), and verified the correctness of the segmentation method, but the classification accuracy needs to be improved [31]. [PERSON] et al. proposed a weighted TFM to image defects through simulation and experimental data, which can improve the signal-to-noise ratio and resolution of ultrasonic imaging of coarse-grained steel and can distinguish the images of adjacent defects [32]. [PERSON] et al. proposed a post-processing algorithm that uses the directivity of laser ultrasounds to suppress artifacts in the full-focus image. The results show that the artifact suppression effect is good, so larger element spacing can be used to reduce the data acquisition time [33]. [PERSON] et al. proposed to replace the traditional FMC acquisition and TFM processing with a single zero plane wave (PW) transmission and conditional generation countermeasure network (cGAN). This method reconstructs the TFM image with the same resolution, and the artifact suppression effect is good. The calculation time and file memory are reduced by 120 times and 75 times, respectively [34]. [PERSON] et al. proposed a fusion factor coefficient for TFM image processing, which adjusts the weights of different modes for image fusion and denoising, thereby maintaining the integrity of the fused image defect information [35]. [PERSON] et al. used Phase Circular Statistical Vectors (PCSVs) for weighted optimization, which improved the quality of fully focused images and suppressed background noise and multi-modal artifacts [36]. [PERSON] et al. proposed a fully focused imaging algorithm based on acoustic tracking, which can suppress artifacts, improve the imaging quality of fully focused detection, and have a defect localization error of less than 0.3 mm [37]. [PERSON] et al. proposed a fast Fourier domain full matrix imaging algorithm that ensuresimage accuracy and resolution in wide-angle situations, suitable for the oblique incidence detection of welds [38]. The reinforcement of branch pipe fillet welds and the difference in branch structures lead to the inapplicability of a variety of detection technologies. In this paper, FMC-TFM technology based on ultrasonic detection is used to detect the small-diameter branch pipe fillet weld, and a set of multi-mode focusing imaging software is developed based on the industrial detection requirements of the branch pipe fillet weld. The pipe surface is convex, with poor surface smoothness and low coupling, resulting in serious image artifacts. Therefore, this paper proposes an image post-processing strategy based on the fusion of regional wavelet denoising and adaptive denoising, which is verified by experiments and can effectively eliminate artifacts. In addition, based on the above work, for the analysis of weld defect characteristics, the minimum rectangle method is used for feature extraction, and the feature analysis is carried out combined with the welding process. The key information, such as the size, location, and type of defects, can be obtained through experimental verification.