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Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system

The integration of image segmentation technology into packaging style design significantly amplifies both the aesthetic allure and practical utility of product packaging design. However, the conventional image segmentation algorithm necessitates a substantial amount of time for image analysis, rende...

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Autor principal: Wang, Jiahao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403159/
https://www.ncbi.nlm.nih.gov/pubmed/37547386
http://dx.doi.org/10.7717/peerj-cs.1451
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author Wang, Jiahao
author_facet Wang, Jiahao
author_sort Wang, Jiahao
collection PubMed
description The integration of image segmentation technology into packaging style design significantly amplifies both the aesthetic allure and practical utility of product packaging design. However, the conventional image segmentation algorithm necessitates a substantial amount of time for image analysis, rendering it susceptible to the loss of vital image features and yielding unsatisfactory segmentation results. Therefore, this study introduces a novel segmentation network, G-Lite-DeepLabV3+, which is seamlessly incorporated into cyber-physical systems (CPS) to enhance the accuracy and efficiency of product packaging image segmentation. In this research, the feature extraction network of DeepLabV3 is replaced with Mobilenetv2, integrating group convolution and attention mechanisms to proficiently process intricate semantic features and improve the network’s responsiveness to valuable characteristics. These adaptations are then deployed within CPS, allowing the G-Lite-DeepLabV3+ network to be seamlessly integrated into the image processing module within CPS. This integration facilitates remote and real-time segmentation of product packaging images in a virtual environment.Experimental findings demonstrate that the G-Lite-DeepLabV3+ network excels at segmenting diverse graphical elements within product packaging images. Compared to the original DeepLabV3+ network, the intersection over union (IoU) metric shows a remarkable increase of 3.1%, while the mean pixel accuracy (mPA) exhibits an impressive improvement of 6.2%. Additionally, the frames per second (FPS) metric experiences a significant boost of 22.1%. When deployed within CPS, the network successfully accomplishes product packaging image segmentation tasks with enhanced efficiency, while maintaining high levels of segmentation accuracy.
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spelling pubmed-104031592023-08-05 Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system Wang, Jiahao PeerJ Comput Sci Algorithms and Analysis of Algorithms The integration of image segmentation technology into packaging style design significantly amplifies both the aesthetic allure and practical utility of product packaging design. However, the conventional image segmentation algorithm necessitates a substantial amount of time for image analysis, rendering it susceptible to the loss of vital image features and yielding unsatisfactory segmentation results. Therefore, this study introduces a novel segmentation network, G-Lite-DeepLabV3+, which is seamlessly incorporated into cyber-physical systems (CPS) to enhance the accuracy and efficiency of product packaging image segmentation. In this research, the feature extraction network of DeepLabV3 is replaced with Mobilenetv2, integrating group convolution and attention mechanisms to proficiently process intricate semantic features and improve the network’s responsiveness to valuable characteristics. These adaptations are then deployed within CPS, allowing the G-Lite-DeepLabV3+ network to be seamlessly integrated into the image processing module within CPS. This integration facilitates remote and real-time segmentation of product packaging images in a virtual environment.Experimental findings demonstrate that the G-Lite-DeepLabV3+ network excels at segmenting diverse graphical elements within product packaging images. Compared to the original DeepLabV3+ network, the intersection over union (IoU) metric shows a remarkable increase of 3.1%, while the mean pixel accuracy (mPA) exhibits an impressive improvement of 6.2%. Additionally, the frames per second (FPS) metric experiences a significant boost of 22.1%. When deployed within CPS, the network successfully accomplishes product packaging image segmentation tasks with enhanced efficiency, while maintaining high levels of segmentation accuracy. PeerJ Inc. 2023-07-10 /pmc/articles/PMC10403159/ /pubmed/37547386 http://dx.doi.org/10.7717/peerj-cs.1451 Text en ©2023 Wang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Wang, Jiahao
Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system
title Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system
title_full Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system
title_fullStr Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system
title_full_unstemmed Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system
title_short Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system
title_sort packaging style design based on visual semantic segmentation technology and intelligent cyber physical system
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403159/
https://www.ncbi.nlm.nih.gov/pubmed/37547386
http://dx.doi.org/10.7717/peerj-cs.1451
work_keys_str_mv AT wangjiahao packagingstyledesignbasedonvisualsemanticsegmentationtechnologyandintelligentcyberphysicalsystem