Ball classification through object detection using deep learning for handball
Dynamic gameplay, fast-paced and fast-changing gameplay, where angle shooting (top and bottom corner) has the best chance of a good goal, are the main aspects of handball. When it comes to the narrow-angle area, the goalkeeper has trouble blocked the goal. Therefore, this research discusses image pr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33637/ http://umpir.ump.edu.my/id/eprint/33637/1/Ball%20classification%20through%20object%20detection%20using%20deep%20learning.pdf |
| _version_ | 1848824305684578304 |
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| author | Arzielah Ashiqin, Alwi Ahmad Najmuddin, Ibrahim Muhammad Nur Aiman, Shapiee Muhammad Ar Rahim, Ibrahim Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin |
| author_facet | Arzielah Ashiqin, Alwi Ahmad Najmuddin, Ibrahim Muhammad Nur Aiman, Shapiee Muhammad Ar Rahim, Ibrahim Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin |
| author_sort | Arzielah Ashiqin, Alwi |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Dynamic gameplay, fast-paced and fast-changing gameplay, where angle shooting (top and bottom corner) has the best chance of a good goal, are the main aspects of handball. When it comes to the narrow-angle area, the goalkeeper has trouble blocked the goal. Therefore, this research discusses image processing to investigate the shooting precision performance analysis to detect the ball's accuracy at high speed. In the handball goal, the participants had to complete 50 successful shots at each of the four target locations. Computer vision will then be implemented through a camera to identify the ball, followed by determining the accuracy of the ball position of floating, net tangle and farthest or smallest using object detection as the accuracy marker. The model will be trained using Deep Learning (DL) models of YOLOv2, YOLOv3, and Faster R-CNN and the best precision models of ball detection accuracy were compared. It was found that the best performance of the accuracy of the classifier Faster R-CNN produces 99% for all ball positions. |
| first_indexed | 2025-11-15T03:10:55Z |
| format | Article |
| id | ump-33637 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:10:55Z |
| publishDate | 2020 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-336372022-04-06T06:17:05Z http://umpir.ump.edu.my/id/eprint/33637/ Ball classification through object detection using deep learning for handball Arzielah Ashiqin, Alwi Ahmad Najmuddin, Ibrahim Muhammad Nur Aiman, Shapiee Muhammad Ar Rahim, Ibrahim Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin T Technology (General) Dynamic gameplay, fast-paced and fast-changing gameplay, where angle shooting (top and bottom corner) has the best chance of a good goal, are the main aspects of handball. When it comes to the narrow-angle area, the goalkeeper has trouble blocked the goal. Therefore, this research discusses image processing to investigate the shooting precision performance analysis to detect the ball's accuracy at high speed. In the handball goal, the participants had to complete 50 successful shots at each of the four target locations. Computer vision will then be implemented through a camera to identify the ball, followed by determining the accuracy of the ball position of floating, net tangle and farthest or smallest using object detection as the accuracy marker. The model will be trained using Deep Learning (DL) models of YOLOv2, YOLOv3, and Faster R-CNN and the best precision models of ball detection accuracy were compared. It was found that the best performance of the accuracy of the classifier Faster R-CNN produces 99% for all ball positions. Penerbit UMP 2020 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33637/1/Ball%20classification%20through%20object%20detection%20using%20deep%20learning.pdf Arzielah Ashiqin, Alwi and Ahmad Najmuddin, Ibrahim and Muhammad Nur Aiman, Shapiee and Muhammad Ar Rahim, Ibrahim and Mohd Azraai, Mohd Razman and Ismail, Mohd Khairuddin (2020) Ball classification through object detection using deep learning for handball. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 2 (2). pp. 49-54. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v2i2.6751 https://doi.org/10.15282/mekatronika.v2i2.6751 |
| spellingShingle | T Technology (General) Arzielah Ashiqin, Alwi Ahmad Najmuddin, Ibrahim Muhammad Nur Aiman, Shapiee Muhammad Ar Rahim, Ibrahim Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Ball classification through object detection using deep learning for handball |
| title | Ball classification through object detection using deep learning for handball |
| title_full | Ball classification through object detection using deep learning for handball |
| title_fullStr | Ball classification through object detection using deep learning for handball |
| title_full_unstemmed | Ball classification through object detection using deep learning for handball |
| title_short | Ball classification through object detection using deep learning for handball |
| title_sort | ball classification through object detection using deep learning for handball |
| topic | T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/33637/ http://umpir.ump.edu.my/id/eprint/33637/ http://umpir.ump.edu.my/id/eprint/33637/ http://umpir.ump.edu.my/id/eprint/33637/1/Ball%20classification%20through%20object%20detection%20using%20deep%20learning.pdf |