Object detection in motion management scenarios based on deep learning.
In athletes' competitions and daily training, in order to further strengthen the athletes' sports level, it is usually necessary to analyze the athletes' sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and posi...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315130 |
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| author | Baocheng Pei Yanan Sun Yebiao Fu Ting Ren |
| author_facet | Baocheng Pei Yanan Sun Yebiao Fu Ting Ren |
| author_sort | Baocheng Pei |
| collection | DOAJ |
| description | In athletes' competitions and daily training, in order to further strengthen the athletes' sports level, it is usually necessary to analyze the athletes' sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and positions of the athletes, sports equipment, field boundaries and other targets in the sports scene. However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. Based on this, we propose for the first time a supervised object detection method for scenarios in the field of motion management. The main contributions of this method include: designing a TSM module that combines temporal offset operation and spatial convolution operation to enhance the network structure's ability to capture temporal information in the motion scene; designing a deformable attention mechanism that enhances the feature extraction capability of individual target actions in the motion scene; designing a decoupling structure that decouples the regression task from the classification task; and using the above approach for object detection in motion management scenarios. The accuracy of target detection in this scenario is greatly. To evaluate the effectiveness of our designed network and proposed methodology, we conduct experiments on open-source datasets. The final comparison experiment shows that our proposed method outperforms all the other seven common target detection networks on the same dataset with a map_0.5 score of 92.298%. In the ablation experiments, the reduction of each module reduces the accuracy of detection. The two types of experiments prove that the proposed method is effective and can achieve better results when applied to motion management detection scenarios. |
| format | Article |
| id | doaj-art-ab6236393af945f3a313385a5ed5d4bf |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ab6236393af945f3a313385a5ed5d4bf2025-01-08T05:31:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031513010.1371/journal.pone.0315130Object detection in motion management scenarios based on deep learning.Baocheng PeiYanan SunYebiao FuTing RenIn athletes' competitions and daily training, in order to further strengthen the athletes' sports level, it is usually necessary to analyze the athletes' sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and positions of the athletes, sports equipment, field boundaries and other targets in the sports scene. However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. Based on this, we propose for the first time a supervised object detection method for scenarios in the field of motion management. The main contributions of this method include: designing a TSM module that combines temporal offset operation and spatial convolution operation to enhance the network structure's ability to capture temporal information in the motion scene; designing a deformable attention mechanism that enhances the feature extraction capability of individual target actions in the motion scene; designing a decoupling structure that decouples the regression task from the classification task; and using the above approach for object detection in motion management scenarios. The accuracy of target detection in this scenario is greatly. To evaluate the effectiveness of our designed network and proposed methodology, we conduct experiments on open-source datasets. The final comparison experiment shows that our proposed method outperforms all the other seven common target detection networks on the same dataset with a map_0.5 score of 92.298%. In the ablation experiments, the reduction of each module reduces the accuracy of detection. The two types of experiments prove that the proposed method is effective and can achieve better results when applied to motion management detection scenarios.https://doi.org/10.1371/journal.pone.0315130 |
| spellingShingle | Baocheng Pei Yanan Sun Yebiao Fu Ting Ren Object detection in motion management scenarios based on deep learning. PLoS ONE |
| title | Object detection in motion management scenarios based on deep learning. |
| title_full | Object detection in motion management scenarios based on deep learning. |
| title_fullStr | Object detection in motion management scenarios based on deep learning. |
| title_full_unstemmed | Object detection in motion management scenarios based on deep learning. |
| title_short | Object detection in motion management scenarios based on deep learning. |
| title_sort | object detection in motion management scenarios based on deep learning |
| url | https://doi.org/10.1371/journal.pone.0315130 |
| work_keys_str_mv | AT baochengpei objectdetectioninmotionmanagementscenariosbasedondeeplearning AT yanansun objectdetectioninmotionmanagementscenariosbasedondeeplearning AT yebiaofu objectdetectioninmotionmanagementscenariosbasedondeeplearning AT tingren objectdetectioninmotionmanagementscenariosbasedondeeplearning |