基于密集连接和多尺度池化的X射线焊缝缺陷分割方法  

X-ray weld defect detection method based on dense connection and multi-scale pooling

作　　者：张勇 王鹏[2] 吕志刚[1,3] 邸若海 李晓艳 李亮亮[3] ZHANG Yong;WANG Peng;LÜZhigang;DI Ruohai;LI Xiaoyan;LI Liangliang(School of Electronic and Information Engineering,Xi'an Technological University,Xi'an 710021,China;Development Planning Service,Xi'an Technological University,Xi'an 710021,China;School of Mechatronic Engineering,Xi'an Technological University,Xi'an 710021,China)

机构地区：[1]西安工业大学电子信息工程学院,陕西西安710021 [2]西安工业大学发展规划处,陕西西安710021 [3]西安工业大学机电工程学院,陕西西安710021

出　　处：《液晶与显示》2024年第1期59-68,共10页Chinese Journal of Liquid Crystals and Displays

基　　金：国家自然科学基金(No.62171360);2022年度陕西高校青年创新团队项目;2023年陕西省高校工程研究中心;西安市军民两用智能测评技术重点实验室;陕西省电子设备智能测试与可靠性评估工程技术研究中心;山东省智慧交通重点实验室(筹)。

摘　　要：为解决X射线底片焊缝缺陷分割精度不高、边界信息模糊的问题,本文提出一种改进的Dilated_Pooling_Unet(DP_Unet)网络分割模型。首先,在上下采样间加入编解码信息提取模块DP_block,旨在下采样后最大限度地保留原始缺陷语义信息及减少连续卷积与池化操作造成的损失;然后,在模型中添加GAM注意力机制重点关注焊缝缺陷部分,有效提升缺陷特征通道的学习能力,降低背景噪声影响;最后,提出一种融合二元交叉熵和DiceLoss的混合损失函数,用于解决网络训练时不均衡的正负类数据问题。实验数据集由公开数据集GDX-ray缺陷数据集组成。实验结果表明,本文所提方法在GDX-ray数据集上有较好表现,Dice值达到了93.45%,与基线算法相比均有显著提高。该方法具有良好的分割性能,优于传统的分割算法,有效提高了底片焊缝缺陷分割精度。In order to solve the problems of low segmentation accuracy and fuzzy boundary information of weld defects in X-ray films,this paper proposes an improved Dilated_Pooling_Unet(DP_Unet)network segmentation model.First of all,the codec information extraction module DP_block is added between up and down sampling,aiming to preserve the original defect semantic information to the greatest extent and reduce the loss caused by continuous convolution and pooling operations after down sampling.In addition,the GAM attention mechanism is added to the model to focus on welding.The seam defect part can effectively improve the learning ability of defect feature channels and reduce the influence of background noise.Finally,a hybrid loss function combining binary cross entropy and DiceLoss is proposed to solve the problems of unbalanced positive and negative data during network training.The experimental dataset is composed of the public dataset GDX-ray defect dataset.Experiments show that the method proposed in this paper has a good performance on the GDX-ray dataset,the Dice value reaches 93.45%,which are significantly improved compared with the baseline algorithm.This method has good segmentation performance,is superior to traditional segmentation algorithms,and effectively improves the segmentation accuracy of negative weld defects.

关 键 词：焊接检测 缺陷分割 DP_Unet 注意力机制 

分 类 号：TE973.3[石油与天然气工程—石油机械设备] TP391.4[自动化与计算机技术—计算机应用技术]