Instance segmentation from small dataset by a dual-layer semantics-based deep learning framework

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外文摘要：Efficient and accurate segmentation of complex microstructures is a critical challenge in establishing process-structure-property(PSP)linkages of materials.Deep learning(DL)-based instance segmentation algorithms show potential in achieving this goal.However,to ensure prediction reliability,the current algorithms usually have complex structures and demand vast training data.To overcome the model complexity and its dependence on the amount of data,we developed an ingenious DL framework based on a simple method called dual-layer semantics.In the framework,a data standardization module was designed to remove extraneous microstructural noise and accentuate desired structural characteristics,while a post-processing module was employed to further improve segmentation accuracy.The framework was successfully applied in a small dataset of bimodal Ti-6Al-4V microstructures with only 112 samples.Compared with the ground truth,it realizes an 86.81％accuracy IoU for the globular αphase and a 94.70％average size distribution similarity for the colony structures.More importantly,only 36 s was taken to handle a 1024 x 1024 micrograph,which is much faster than the treatment of experienced experts(usually 900 s).The framework proved reliable,interpretable,and scalable,enabling its utilization in complex microstructures to deepen the understanding of PSP linkages.

外文关键词：

instance segmentationdual-layer semanticsdeep learningsmall datasetTi-6Al-4V

作者：

CHEN YiMing、LI JianWei、HU XiaoBing、LIU YiRui、MA JianKai、XING Chen、LI JunJie、WANG ZhiJun、WANG JinCheng

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作者单位：

State Key Laboratory of Solidification Processing,Northwestern Polytechnical University,Xi'an 710072,China

基金：

National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaResearch Fund of the State Key Laboratory of Solidification Processing(NPU),ChinaPractice and Innovation Funds for Graduate Students of Northwestern Polytechnical University

项目编号：

2023YFB460650251871183518742452020-TS-06

出版年：

2024

DOI：

10.1007/s11431-023-2646-3

中国科学:技术科学(英文版)

中国科学院

中国科学:技术科学(英文版)

CSTPCDEI

影响因子：1.056

ISSN：1674-7321

年,卷(期)：2024.67(9)