In recent years,there has been tremendous progress in the development of deep-learning-based approaches for optical metrology,which introduce various deep neural networks(DNNs)for many optical metrology tasks,such as ...In recent years,there has been tremendous progress in the development of deep-learning-based approaches for optical metrology,which introduce various deep neural networks(DNNs)for many optical metrology tasks,such as fringe analysis,phase unwrapping,and digital image correlation.However,since different DNN models have their own strengths and limitations,it is difficult for a single DNN to make reliable predictions under all possible scenarios.In this work,we introduce ensemble learning into optical metrology,which combines the predictions of multiple DNNs to significantly enhance the accuracy and reduce the generalization error for the task of fringe-pattern analysis.First,several state-of-the-art base models of different architectures are selected.A K-fold average ensemble strategy is developed to train each base model multiple times with different data and calculate the mean prediction within each base model.Next,an adaptive ensemble strategy is presented to further combine the base models by building an extra DNN to fuse the features extracted from these mean predictions in an adaptive and fully automatic way.Experimental results demonstrate that ensemble learning could attain superior performance over state-of-the-art solutions,including both classic and conventional single-DNN-based methods.Our work suggests that by resorting to collective wisdom,ensemble learning offers a simple and effective solution for overcoming generalization challenges and boosts the performance of data-driven optical metrology methods.展开更多
基金supported by the National Key R&D Program of China(Grant Nos.2022YFB2804600 and 2022YFB2804605)the National Natural Science Foundation of China(Grant Nos.62075096 and U21B2033)+4 种基金the Leading Technology of Jiangsu Basic Research Plan(Grant No.BK20192003)the“333 Engineering”Research Project of Jiangsu Province(Grant No.BRA2016407)the Jiangsu Provincial“Belt and Road Initiative”Cooperation Project(Grant No.BZ2020007)the Fundamental Research Funds for the Central Universities(Grant No.30921011208)the National Major Scientific Instrument Development Project(Grant No.62227818).
文摘In recent years,there has been tremendous progress in the development of deep-learning-based approaches for optical metrology,which introduce various deep neural networks(DNNs)for many optical metrology tasks,such as fringe analysis,phase unwrapping,and digital image correlation.However,since different DNN models have their own strengths and limitations,it is difficult for a single DNN to make reliable predictions under all possible scenarios.In this work,we introduce ensemble learning into optical metrology,which combines the predictions of multiple DNNs to significantly enhance the accuracy and reduce the generalization error for the task of fringe-pattern analysis.First,several state-of-the-art base models of different architectures are selected.A K-fold average ensemble strategy is developed to train each base model multiple times with different data and calculate the mean prediction within each base model.Next,an adaptive ensemble strategy is presented to further combine the base models by building an extra DNN to fuse the features extracted from these mean predictions in an adaptive and fully automatic way.Experimental results demonstrate that ensemble learning could attain superior performance over state-of-the-art solutions,including both classic and conventional single-DNN-based methods.Our work suggests that by resorting to collective wisdom,ensemble learning offers a simple and effective solution for overcoming generalization challenges and boosts the performance of data-driven optical metrology methods.
