通过平滑单热编码改进铸件数字射线照片中的散射缺陷分级

IF 2.6 3区 材料科学 Q2 METALLURGY & METALLURGICAL ENGINEERING
Han Yu, Xingjie Li, Xue Hao, Zhaowei Song, Shangyu Liu, Xinyue Li, Chunyu Hou, Huasheng Xie
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引用次数: 0

摘要

要保证铸件质量并提高铸件生产的盈利能力,就必须对不连续性进行精确分级。最近利用计算机视觉的分级方法是通过执行单标签图像分类或回归来实现的,这就失去了内在的序数关系。受此启发,我们提出了一种针对序数变量的标签平滑技术,将每个缺陷实例的等级转换为离散概率分布,旨在模拟噪声标签和序数关系。此外,我们还设计了一种基于多任务学习的卷积神经网络框架。通过同时学习等级标签分布和直接回归等级,该框架的整体性能优于单任务网络。最后,我们构建了一个铸造气孔缺陷分级数据集。与传统的单标签图像分类或回归算法相比,在该数据集上的实验结果凸显了我们提出的方法的显著优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Improving Scattered Defect Grading in Castings Digital Radiographs via Smoothing the One-Hot Encoding

Improving Scattered Defect Grading in Castings Digital Radiographs via Smoothing the One-Hot Encoding

Ensuring the precise grading of discontinuities is imperative to guarantee the quality of castings and enhance profitability in casting production. Recent grading methods leveraging computer vision are advanced by performing a single-label image classification or regression, which loses the intrinsically ordinal relationship. Motivated by this observation, we propose a label smoothing technology for ordinal variables to convert the level of each defect instance into a discrete probability distribution, aiming to model the noise label and ordinal relationship. Furthermore, we design a convolutional neural network framework based on multi-task learning. This framework, by simultaneously learning the level label distribution and regressing the level directly, outperforms a single-task network in terms of overall performance. Finally, we construct a casting gas porosity defect grading dataset. Experimental results on this dataset highlight the significant advantages of our proposed method compared to traditional single-label image classification or regression algorithms.

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来源期刊
International Journal of Metalcasting
International Journal of Metalcasting 工程技术-冶金工程
CiteScore
4.20
自引率
42.30%
发文量
174
审稿时长
>12 weeks
期刊介绍: The International Journal of Metalcasting is dedicated to leading the transfer of research and technology for the global metalcasting industry. The quarterly publication keeps the latest developments in metalcasting research and technology in front of the scientific leaders in our global industry throughout the year. All papers published in the the journal are approved after a rigorous peer review process. The editorial peer review board represents three international metalcasting groups: academia (metalcasting professors), science and research (personnel from national labs, research and scientific institutions), and industry (leading technical personnel from metalcasting facilities).
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