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Lightweight saliency detection method for real-time localization of livestock meat bones
Existing salient object detection networks are large, have many parameters, are bulky and take up a lot of computational resources. Seriously hinder its application and promotion in boning robot. To solve this problem, this paper proposes a lightweight saliency detection algorithm for real-time loca...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10024766/ https://www.ncbi.nlm.nih.gov/pubmed/36934170 http://dx.doi.org/10.1038/s41598-023-31551-6 |
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author | Xu, Tao Zhao, Weishuo Cai, Lei Shi, Xiaoli Wang, Xinfa |
author_facet | Xu, Tao Zhao, Weishuo Cai, Lei Shi, Xiaoli Wang, Xinfa |
author_sort | Xu, Tao |
collection | PubMed |
description | Existing salient object detection networks are large, have many parameters, are bulky and take up a lot of computational resources. Seriously hinder its application and promotion in boning robot. To solve this problem, this paper proposes a lightweight saliency detection algorithm for real-time localization of livestock meat bones. First, a lightweight feature extraction network based on multi-scale attention is constructed in the encoding stage. To ensure that more adequate salient object features are extracted with fewer parameters. Second, the fusion of jump connections is introduced in the decoding phase. Used to capture fine-grained semantics and coarse-grained semantics at full scale. Finally, we added a residual refinement module at the end of the backbone network. For optimizing salient target regions and boundaries. Experimental results on both publicly available datasets and self-made Pig leg X-ray (PLX) datasets show that. The proposed method is capable of ensuring first-class detection accuracy with 40 times less parameters than the conventional model. In the most challenging SOD dataset. The proposed algorithm in this paper achieves a value of Fωβ of 0.699. And the segmentation of livestock bones can be effectively performed on the homemade PLX dataset. Our model has a detection speed of 5fps on industrial control equipment. |
format | Online Article Text |
id | pubmed-10024766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100247662023-03-20 Lightweight saliency detection method for real-time localization of livestock meat bones Xu, Tao Zhao, Weishuo Cai, Lei Shi, Xiaoli Wang, Xinfa Sci Rep Article Existing salient object detection networks are large, have many parameters, are bulky and take up a lot of computational resources. Seriously hinder its application and promotion in boning robot. To solve this problem, this paper proposes a lightweight saliency detection algorithm for real-time localization of livestock meat bones. First, a lightweight feature extraction network based on multi-scale attention is constructed in the encoding stage. To ensure that more adequate salient object features are extracted with fewer parameters. Second, the fusion of jump connections is introduced in the decoding phase. Used to capture fine-grained semantics and coarse-grained semantics at full scale. Finally, we added a residual refinement module at the end of the backbone network. For optimizing salient target regions and boundaries. Experimental results on both publicly available datasets and self-made Pig leg X-ray (PLX) datasets show that. The proposed method is capable of ensuring first-class detection accuracy with 40 times less parameters than the conventional model. In the most challenging SOD dataset. The proposed algorithm in this paper achieves a value of Fωβ of 0.699. And the segmentation of livestock bones can be effectively performed on the homemade PLX dataset. Our model has a detection speed of 5fps on industrial control equipment. Nature Publishing Group UK 2023-03-18 /pmc/articles/PMC10024766/ /pubmed/36934170 http://dx.doi.org/10.1038/s41598-023-31551-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Xu, Tao Zhao, Weishuo Cai, Lei Shi, Xiaoli Wang, Xinfa Lightweight saliency detection method for real-time localization of livestock meat bones |
title | Lightweight saliency detection method for real-time localization of livestock meat bones |
title_full | Lightweight saliency detection method for real-time localization of livestock meat bones |
title_fullStr | Lightweight saliency detection method for real-time localization of livestock meat bones |
title_full_unstemmed | Lightweight saliency detection method for real-time localization of livestock meat bones |
title_short | Lightweight saliency detection method for real-time localization of livestock meat bones |
title_sort | lightweight saliency detection method for real-time localization of livestock meat bones |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10024766/ https://www.ncbi.nlm.nih.gov/pubmed/36934170 http://dx.doi.org/10.1038/s41598-023-31551-6 |
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