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Bass detection model based on improved YOLOv5 in circulating water system
The feeding amount of bass farming is closely related to the number of bass. It is of great significance to master the number of bass to achieve accurate feeding and improve the economic benefits of the farm. In view of the interference caused by the problems of multiple targets and target occlusion...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042332/ https://www.ncbi.nlm.nih.gov/pubmed/36972258 http://dx.doi.org/10.1371/journal.pone.0283671 |
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author | Xu, Longqin Deng, Hao Cao, Yingying Liu, Wenjun He, Guohuang Fan, Wenting Wei, Tangliang Cao, Liang Liu, Tonglai Liu, Shuangyin |
author_facet | Xu, Longqin Deng, Hao Cao, Yingying Liu, Wenjun He, Guohuang Fan, Wenting Wei, Tangliang Cao, Liang Liu, Tonglai Liu, Shuangyin |
author_sort | Xu, Longqin |
collection | PubMed |
description | The feeding amount of bass farming is closely related to the number of bass. It is of great significance to master the number of bass to achieve accurate feeding and improve the economic benefits of the farm. In view of the interference caused by the problems of multiple targets and target occlusion in bass data for bass detection, this paper proposes a bass target detection model based on improved YOLOV5 in circulating water system. Firstly, acquiring by HD cameras, Mosaic-8, a data augmentation method, is utilized to expand datasets and improve the generalization ability of the model. And K-means clustering algorithm is applied to generate suitable coordinates of prior boxes to improve training efficiency. Secondly, Coordinate Attention mechanism (CA) is introduced into backbone feature extraction network and neck feature fusion network to enhance attention to targets of interest. Finally, Soft-NMS algorithm replaces Non-Maximum Suppression algorithm (NMS) to re-screen prediction boxes and keep targets with higher overlap, which effectively solves the problems of missed detection and false detection. The experiments show that the proposed model can reach 98.09% in detection accuracy and detection speed reaches 13.4ms. The proposed model can help bass farmers under the circulating water system to accurately grasp the number of bass, which has important application value to realize accurate feeding and water conservation. |
format | Online Article Text |
id | pubmed-10042332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100423322023-03-28 Bass detection model based on improved YOLOv5 in circulating water system Xu, Longqin Deng, Hao Cao, Yingying Liu, Wenjun He, Guohuang Fan, Wenting Wei, Tangliang Cao, Liang Liu, Tonglai Liu, Shuangyin PLoS One Research Article The feeding amount of bass farming is closely related to the number of bass. It is of great significance to master the number of bass to achieve accurate feeding and improve the economic benefits of the farm. In view of the interference caused by the problems of multiple targets and target occlusion in bass data for bass detection, this paper proposes a bass target detection model based on improved YOLOV5 in circulating water system. Firstly, acquiring by HD cameras, Mosaic-8, a data augmentation method, is utilized to expand datasets and improve the generalization ability of the model. And K-means clustering algorithm is applied to generate suitable coordinates of prior boxes to improve training efficiency. Secondly, Coordinate Attention mechanism (CA) is introduced into backbone feature extraction network and neck feature fusion network to enhance attention to targets of interest. Finally, Soft-NMS algorithm replaces Non-Maximum Suppression algorithm (NMS) to re-screen prediction boxes and keep targets with higher overlap, which effectively solves the problems of missed detection and false detection. The experiments show that the proposed model can reach 98.09% in detection accuracy and detection speed reaches 13.4ms. The proposed model can help bass farmers under the circulating water system to accurately grasp the number of bass, which has important application value to realize accurate feeding and water conservation. Public Library of Science 2023-03-27 /pmc/articles/PMC10042332/ /pubmed/36972258 http://dx.doi.org/10.1371/journal.pone.0283671 Text en © 2023 Xu et al 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, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Xu, Longqin Deng, Hao Cao, Yingying Liu, Wenjun He, Guohuang Fan, Wenting Wei, Tangliang Cao, Liang Liu, Tonglai Liu, Shuangyin Bass detection model based on improved YOLOv5 in circulating water system |
title | Bass detection model based on improved YOLOv5 in circulating water system |
title_full | Bass detection model based on improved YOLOv5 in circulating water system |
title_fullStr | Bass detection model based on improved YOLOv5 in circulating water system |
title_full_unstemmed | Bass detection model based on improved YOLOv5 in circulating water system |
title_short | Bass detection model based on improved YOLOv5 in circulating water system |
title_sort | bass detection model based on improved yolov5 in circulating water system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042332/ https://www.ncbi.nlm.nih.gov/pubmed/36972258 http://dx.doi.org/10.1371/journal.pone.0283671 |
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