文献类型：期刊文献

中文题名：基于改进U-Net3+的相控阵超声图像语义分割

英文题名：Semantic Segmentation of Phased Array Ultrasound Images Based on Improved U-Net3+

作者：毛鑫玥[1];王慧锋[1];周家乐[1];顾震[1];颜秉勇[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2025

卷号：51

期号：2

起止页码：242

中文期刊名：华东理工大学学报(自然科学版)

外文期刊名：Journal of East China University of Science and Technology

收录：;北大核心:【北大核心2023】；

基金：青年科学基金项目(61906068)。

语种：中文

中文关键词：相控阵超声图像;图像语义分割;U-Net3+;注意力机制;残差网络

外文关键词：phased array ultrasound images;image semantic segmentation;U-Net3+;attention mechanism;residual network

摘要：超声相控阵成像已广泛应用于聚乙烯燃气管道的焊接缺陷检测中,随着机器视觉技术的快速发展,利用机器辅助或自动化分析超声图像能极大地提高缺陷检测速度,减少人为判断失误的发生。在基于超声图像的焊接缺陷检测技术中,图像语义分割精度对缺陷类别和严重等级的判定至关重要。本文在U-Net3+网络的基础上提出一种融入残差及注意力机制的改进模型,并应用于电熔焊接缺陷检测的相控阵超声图像语义分割。首先,改进模型通过在编码器各层之间采用残差结构来提升编码器的图像特征提取能力;其次,通过在跳跃连接中引入卷积块注意力模块(Convolutional Block Attention Module,CBAM),加强模型对原始图像信息的利用率,使模型更易聚焦于原始图像中的有效区域。实验结果表明,改进后的模型在电熔焊接超声图像上具有良好的分割效果,在Dice、mIoU两项指标上,相比U-Net分别提升了8.81%和12.84%;相比U-Net3+的分割效果分别提升了1.09%和1.81%。
Ultrasonic phased array images have been widely used in the welding defect detection of polyethylene gas pipelines.With the rapid development of machine vision technology,using machine assisted or automated analysis of ultrasonic images can greatly improve the defect detection speed and reduce the occurrence of human judgment errors.In the welding defect detection technology based on ultrasound images,the accuracy of image semantic segmentation is crucial for determining the defect categories and severity levels.This paper proposes an improved model incorporating residual modules and attention mechanism based on the U-Net3+network,and applies it to the semantic segmentation of phased array ultrasonic image for defect detection in electrofusion welding.Firstly,the improved model enhances the image feature extraction ability of the encoder by adopting a residual structure between each layer of the encoder.Secondly,by introducing Convolutional Block Attention Module(CBAM)in the skip connection,the model’s utilization of original image information is strengthened,making it easier for the model to focus on effective regions in the original image.The experimental results show that the improved model has good segmentation performance on ultrasonic images of electrofusion welding,with improvements of 8.81%and 12.84%in Dice and mIoU indicators compared to U-Net,respectively.Compared to U-Net3+,the segmentation performance is improved by 1.09%and 1.81%,respectively.