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Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN

The grade of wheat quality depends on the proportion of unsound kernels. Therefore, the rapid detection of unsound wheat kernels is important for wheat rating and evaluation. However, in practice, unsound kernels are hand-picked, which makes the process time-consuming and inefficient. Meanwhile, met...

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Detalles Bibliográficos
Autores principales: Shen, Ran, Zhen, Tong, Li, Zhihui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099221/
https://www.ncbi.nlm.nih.gov/pubmed/37050436
http://dx.doi.org/10.3390/s23073379
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author Shen, Ran
Zhen, Tong
Li, Zhihui
author_facet Shen, Ran
Zhen, Tong
Li, Zhihui
author_sort Shen, Ran
collection PubMed
description The grade of wheat quality depends on the proportion of unsound kernels. Therefore, the rapid detection of unsound wheat kernels is important for wheat rating and evaluation. However, in practice, unsound kernels are hand-picked, which makes the process time-consuming and inefficient. Meanwhile, methods based on traditional image processing cannot divide adherent particles well. To solve the above problems, this paper proposed an unsound wheat kernel recognition algorithm based on an improved mask RCNN. First, we changed the feature pyramid network (FPN) to a bottom-up pyramid network to strengthen the low-level information. Then, an attention mechanism (AM) module was added between the feature extraction network and the pyramid network to improve the detection accuracy for small targets. Finally, the regional proposal network (RPN) was optimized to improve the prediction performance. Experiments showed that the improved mask RCNN algorithm could identify the unsound kernels more quickly and accurately while handling adhesion problems well. The precision and recall were 86% and 91%, respectively, and the inference time on the test set with about 200 targets for each image was 7.83 s. Additionally, we compared the improved model with other existing segmentation models, and experiments showed that our model achieved higher accuracy and performance than the other models, laying the foundation for wheat grading.
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spelling pubmed-100992212023-04-14 Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN Shen, Ran Zhen, Tong Li, Zhihui Sensors (Basel) Article The grade of wheat quality depends on the proportion of unsound kernels. Therefore, the rapid detection of unsound wheat kernels is important for wheat rating and evaluation. However, in practice, unsound kernels are hand-picked, which makes the process time-consuming and inefficient. Meanwhile, methods based on traditional image processing cannot divide adherent particles well. To solve the above problems, this paper proposed an unsound wheat kernel recognition algorithm based on an improved mask RCNN. First, we changed the feature pyramid network (FPN) to a bottom-up pyramid network to strengthen the low-level information. Then, an attention mechanism (AM) module was added between the feature extraction network and the pyramid network to improve the detection accuracy for small targets. Finally, the regional proposal network (RPN) was optimized to improve the prediction performance. Experiments showed that the improved mask RCNN algorithm could identify the unsound kernels more quickly and accurately while handling adhesion problems well. The precision and recall were 86% and 91%, respectively, and the inference time on the test set with about 200 targets for each image was 7.83 s. Additionally, we compared the improved model with other existing segmentation models, and experiments showed that our model achieved higher accuracy and performance than the other models, laying the foundation for wheat grading. MDPI 2023-03-23 /pmc/articles/PMC10099221/ /pubmed/37050436 http://dx.doi.org/10.3390/s23073379 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shen, Ran
Zhen, Tong
Li, Zhihui
Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN
title Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN
title_full Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN
title_fullStr Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN
title_full_unstemmed Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN
title_short Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN
title_sort segmentation of unsound wheat kernels based on improved mask rcnn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099221/
https://www.ncbi.nlm.nih.gov/pubmed/37050436
http://dx.doi.org/10.3390/s23073379
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