文献类型：期刊文献

英文题名：Improved U-Net-Based Pore Segmentation Method for Nano-Ag Coupling Layer

作者：Gu, Yiqing[1];Wang, Mingyuan[1];Jia, Jiuhong[1];Tu, Shan-Tung[1]

机构：[1]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China

年份：2025

卷号：25

期号：17

起止页码：33488

外文期刊名：IEEE SENSORS JOURNAL

收录：;EI(收录号：20250917941667);WOS:【SCI-EXPANDED(收录号:WOS:001562587700044)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 52175138 and in part by the Soil-Transplanting Project of Xing Liao Talent Program under Grant XLYC2204030.

语种：英文

外文关键词：Couplings; Image segmentation; Feature extraction; Transducers; Adaptation models; Training; Decoding; Image edge detection; Noise; Attention mechanisms; Atrous spatial pyramid pooling (ASPP); improved U-Net; nano-Ag coupling layer; pore segmentation; squeeze-and-excitation (SE); structural health monitoring (SHM); ultrasonic transducer

摘要：Nano-Ag coupling layer has been successfully applied in ultrasonic transducers, where the morphology of internal pores significantly affects the coupling performance. In this work, a nano-Ag coupling layer pore segmentation method based on improved U-Net is proposed. The proposed method enhances the traditional U-Net architecture by incorporating atrous spatial pyramid pooling (ASPP), squeeze-and-excitation (SE) blocks, attention mechanisms, and deep supervision strategies to augment the model's feature extraction and representation capabilities. Experiments conducted on a dataset of scanning electron microscope (SEM) images of nano-Ag coupling layers demonstrate that the proposed model outperforms classic segmentation models, such as U-Net, achieving a mean intersection over union (Mean IoU) of 0.663 (compared to 0.580), a mean dice coefficient of 0.759 (compared to 0.700), and other superior metrics, including a mean precision of 0.793, a mean pixel accuracy of 0.988, and a lower mean Hausdorff distance of 4.857 (compared to 6.918). Ablation studies further verify the contributions of each improvement module to the model's enhanced performance. The methodology proposed in this work provides an effective solution for the precise segmentation of pores in nano-Ag coupling layers, supporting quantitative characterization and prediction of the coupling performance in ultrasonic transducers.