Optimization of table tennis target detection algorithm guided by multi-scale feature fusion of deep learning
This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper...
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| Published in | Scientific reports Vol. 14; no. 1; pp. 1401 - 16 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
16.01.2024
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. |
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| AbstractList | This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. Abstract This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life.This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. |
| ArticleNumber | 1401 |
| Author | Rong, Zhang |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38228726$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3390/app13148290 10.3390/rs11040382 10.1038/s41592-023-01775-5 10.3390/rs13224706 10.3390/s19020281 10.1080/2150704X.2019.1706007 10.1109/TPAMI.2019.2956516 10.1021/acsnano.2c02609 10.3390/en12071204 10.3390/s20041142 10.3390/s23218693 10.3390/rs13020281 10.3390/electronics8050481 10.3390/app12199761 10.3390/rs12030389 10.3390/e22080811 10.1109/TPAMI.2020.2983686 10.1371/journal.pone.0245259 10.1007/s00521-021-06391-y 10.1049/sil2.12194 10.3390/rs12050872 10.3390/s23031726 10.3390/ijgi11030158 10.3390/rs14020420 10.3390/rs12050762 10.3390/rs11050594 10.3390/rs13101909 10.3390/s22103935 10.3390/rs14010143 10.1007/s13042-021-01496-1 10.3390/app122211534 10.1109/JAS.2020.1003099 10.1109/TITS.2022.3164407 10.1109/JBHI.2020.3042069 10.1007/s40747-022-00859-7 10.3390/s22031215 10.3390/s20154276 10.3390/app132412977 10.3390/app122312116 10.3390/s20185315 10.3390/s22103813 10.3390/rs13050847 10.1002/cpe.6517 |
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| Snippet | This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the... Abstract This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the... |
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| SubjectTerms | 639/705/117 639/705/258 Accuracy Algorithms Athletes Deep learning Humanities and Social Sciences multidisciplinary Neural networks Science Science (multidisciplinary) Table tennis |
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| Title | Optimization of table tennis target detection algorithm guided by multi-scale feature fusion of deep learning |
| URI | https://link.springer.com/article/10.1038/s41598-024-51865-3 https://www.ncbi.nlm.nih.gov/pubmed/38228726 https://www.proquest.com/docview/2915455322 https://www.proquest.com/docview/2915990404 https://pubmed.ncbi.nlm.nih.gov/PMC10792085 https://doaj.org/article/60e59ac15b3f46cf96a8a858ada53131 |
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