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A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network
College students learn words always under both teachers' and school administrators' control. Based on multi-modal discourse analysis theory, the analysis of English words under the synergy of different modalities, students improve the motivation and effectiveness of word learning, but ther...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200988/ https://www.ncbi.nlm.nih.gov/pubmed/35720773 http://dx.doi.org/10.3389/fncom.2022.895680 |
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author | Pan, Leying |
author_facet | Pan, Leying |
author_sort | Pan, Leying |
collection | PubMed |
description | College students learn words always under both teachers' and school administrators' control. Based on multi-modal discourse analysis theory, the analysis of English words under the synergy of different modalities, students improve the motivation and effectiveness of word learning, but there are still some problems, such as the lack of visual modal memory of pictures, incomplete word meanings, little interaction between users, and lack of resource expansion function. To this end, this paper proposes a stepped image semantic segmentation network structure based on multi-scale feature fusion and boundary optimization. The network aims at improving the accuracy of the network model, optimizing the spatial pooling pyramid module in Deeplab V3+ network, using a new activation function Funnel ReLU (FReLU) for vision tasks to replace the original non-linear activation function to obtain accuracy compensation, improving the overall image segmentation accuracy through accurate prediction of the boundaries of each class, reducing the intra-class error in the prediction results. The accuracy compensation is obtained by replacing the original linear activation function with FReLU. Experimental results on the Englishhnd dataset demonstrate that the improved network can achieve 96.35% accuracy for English characters with the same network parameters, training data and test data. |
format | Online Article Text |
id | pubmed-9200988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92009882022-06-17 A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network Pan, Leying Front Comput Neurosci Neuroscience College students learn words always under both teachers' and school administrators' control. Based on multi-modal discourse analysis theory, the analysis of English words under the synergy of different modalities, students improve the motivation and effectiveness of word learning, but there are still some problems, such as the lack of visual modal memory of pictures, incomplete word meanings, little interaction between users, and lack of resource expansion function. To this end, this paper proposes a stepped image semantic segmentation network structure based on multi-scale feature fusion and boundary optimization. The network aims at improving the accuracy of the network model, optimizing the spatial pooling pyramid module in Deeplab V3+ network, using a new activation function Funnel ReLU (FReLU) for vision tasks to replace the original non-linear activation function to obtain accuracy compensation, improving the overall image segmentation accuracy through accurate prediction of the boundaries of each class, reducing the intra-class error in the prediction results. The accuracy compensation is obtained by replacing the original linear activation function with FReLU. Experimental results on the Englishhnd dataset demonstrate that the improved network can achieve 96.35% accuracy for English characters with the same network parameters, training data and test data. Frontiers Media S.A. 2022-06-02 /pmc/articles/PMC9200988/ /pubmed/35720773 http://dx.doi.org/10.3389/fncom.2022.895680 Text en Copyright © 2022 Pan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Pan, Leying A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network |
title | A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network |
title_full | A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network |
title_fullStr | A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network |
title_full_unstemmed | A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network |
title_short | A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network |
title_sort | study of english learning vocabulary detection based on image semantic segmentation fusion network |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200988/ https://www.ncbi.nlm.nih.gov/pubmed/35720773 http://dx.doi.org/10.3389/fncom.2022.895680 |
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