Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform
Abstract Volleyball training robots play an important role in modern sports training, and their automatic serving technology can simulate serving modes in different scenarios, providing athletes with diverse training programs. However, due to the characteristics of volleyball, such as small size, fa...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00450-2 |
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| _version_ | 1849764566360129536 |
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| author | Tao Sun Xiaolong He Jiajun Zhang |
| author_facet | Tao Sun Xiaolong He Jiajun Zhang |
| author_sort | Tao Sun |
| collection | DOAJ |
| description | Abstract Volleyball training robots play an important role in modern sports training, and their automatic serving technology can simulate serving modes in different scenarios, providing athletes with diverse training programs. However, due to the characteristics of volleyball, such as small size, fast speed, and susceptibility to occlusion and noise interference in complex backgrounds, traditional object detection methods are hard to meet their real-time and accuracy requirements. An automatic serving method for volleyball training robots is proposed based on You Only Look Once v5 and Hough transform. By introducing convolutional block attention module to optimize feature extraction and focus on key areas, a weighted bi-directional feature pyramid network is taken to fuse multi-scale features, and gradient optimized Hough transform is used to optimize the accuracy of target localization. The model outperformed the comparison model in accuracy, root mean square error, recall, and F1-score, with a detection accuracy of 92.3% and a root mean square error of only 0.18. The success rate in capturing dynamic targets reached 95.6%, and the trajectory tracking error was reduced to 0.1 m, significantly improving the automation level in volleyball sports. The method has good accuracy and real-time performance, providing an efficient and accurate automatic serving method for volleyball training robots. |
| format | Article |
| id | doaj-art-3cb4a85c4afd4e219499616baa26371d |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-3cb4a85c4afd4e219499616baa26371d2025-08-20T03:05:06ZengSpringerDiscover Artificial Intelligence2731-08092025-08-015112010.1007/s44163-025-00450-2Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transformTao Sun0Xiaolong He1Jiajun Zhang2Physical Education Teaching and Research Office, Liren College, Yanshan UniversitySchool of Mechanical Engineering, Yanshan UniversityPhysical Education Department, Tianjin College of Media ＆ ArtsAbstract Volleyball training robots play an important role in modern sports training, and their automatic serving technology can simulate serving modes in different scenarios, providing athletes with diverse training programs. However, due to the characteristics of volleyball, such as small size, fast speed, and susceptibility to occlusion and noise interference in complex backgrounds, traditional object detection methods are hard to meet their real-time and accuracy requirements. An automatic serving method for volleyball training robots is proposed based on You Only Look Once v5 and Hough transform. By introducing convolutional block attention module to optimize feature extraction and focus on key areas, a weighted bi-directional feature pyramid network is taken to fuse multi-scale features, and gradient optimized Hough transform is used to optimize the accuracy of target localization. The model outperformed the comparison model in accuracy, root mean square error, recall, and F1-score, with a detection accuracy of 92.3% and a root mean square error of only 0.18. The success rate in capturing dynamic targets reached 95.6%, and the trajectory tracking error was reduced to 0.1 m, significantly improving the automation level in volleyball sports. The method has good accuracy and real-time performance, providing an efficient and accurate automatic serving method for volleyball training robots.https://doi.org/10.1007/s44163-025-00450-2VolleyballYOLOv5Hough transform optimizationSmall target detectionAutomatic serving |
| spellingShingle | Tao Sun Xiaolong He Jiajun Zhang Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform Discover Artificial Intelligence Volleyball YOLOv5 Hough transform optimization Small target detection Automatic serving |
| title | Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform |
| title_full | Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform |
| title_fullStr | Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform |
| title_full_unstemmed | Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform |
| title_short | Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform |
| title_sort | automatic serving method of volleyball training robot based on improved yolov5 and improved hough transform |
| topic | Volleyball YOLOv5 Hough transform optimization Small target detection Automatic serving |
| url | https://doi.org/10.1007/s44163-025-00450-2 |
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