Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1785025009122541568 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10099221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT shenran segmentationofunsoundwheatkernelsbasedonimprovedmaskrcnn AT zhentong segmentationofunsoundwheatkernelsbasedonimprovedmaskrcnn AT lizhihui segmentationofunsoundwheatkernelsbasedonimprovedmaskrcnn |