## 6 Conclusions  
This paper investigated the ultrasonic testing of near-surface defects in the polyethylene pipeline hot-melt but welds. Firstly, a novel method for detecting and locating near-surface defects through SST was proposed. Then, a lightweight CNN model was designed for identifying the type of near-surface defects, employing the DenseNet structure as the backbone network and combining DSC and CBAM. The combination of these two techniques has facilitated the detection, localization, and identification of both near-surface defects and general defects. Here are three conclusions that can be drawn:  
1. The SST, which combines CWT and rearrangement algorithms, achieves a more refined time-frequency distribution of the near-surface defect signal through energy concentration. By extracting the maximum of time-frequency distribution of the near-surface defect area, clearer instantaneous energy changes can be obtained for locating the defect. Significantly, the SST's notable benefits extend beyond the detection of near-surface defects and can be applied to a broader range of similar overlapping signal analysis problems. 2. The proposed model is capable of achieving accurate identification of defects regardless of whether they overlap with blind zone signals. Moreover, the model features significantly lower parameter quantity, computational complexity, and model size than classical models, including ResNet18, VGG16, and Inception-v3. Additionally, it also outperforms lightweight models like SqueezeNet, ShuffleNet-v2, and MobileNet-v3-small in terms of accuracy. This suggests that many large models contain an array of redundant parameters. 3. The visualization results have demonstrated that the model excels in capturing the essential time-frequency information compared to other models, making it a reliable choice. Meanwhile, the visualization has also revealed that even models showcasing excellent performance may miss out on vital information areas. Therefore, researchers must conduct an interpretable analysis of the model in addition to traditional evaluation indices, particularly when analyzing images with physical significance. Such analysis will help them investigate the reliability of the model's underlying judgment-making process. This study conducted the detection and identification of typical volumetric defects and planar defects within and outside the transducer's blind zone, but further consideration of defect size, other defect types and other pipeline materials could yield valuable insights into this field. For example, the focus area is becoming broader than the low-frequencyrange in deep dense layers (0-5 MHz), which already encompasses a frequency of 10 MHz. This prompts the question of whether there are any changes occurring near 10 MHz that are related to the presence of defects, but are currently unknown to us. This is also a necessary area of investigation for future studies. The error of the model mainly comes from pure manual operation when collecting signals. In this case, if there is a change in the handheld posture, the waveform will be inconsistent, so the defect position can only be obtained from the post-processing or defect positioning method, resulting in deviation. If mechanical devices, such as stepper motors, can be used to control the distance of each movement, then it is easier to achieve the accurate labeling of defect positions. Finally, dataset expansion, movement step control and model performance improvement still need to be carried out. Conceptualization, C.C., H.H. and M.S. ; Data curation, C.C. and S.Z. ; Formal analysis, C.C. and S.Z. ; Funding acquisition, H.H. and M.S. ; Investigation, C.C. and S.Z. ; Methodology, C.C. and S.Z. ; Project administration, M.S. ; Resources, H.H. and M.S. ; Software, C.J. ; Supervision, M.S. ; Validation, C.J. and Z.Z. ; Visualization, Z.Z. ; Writing--original draft, C.C. ; Writing--review & editing, S.Z. All authors have read and agreed to the published version of the manuscript.