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)
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Abstract
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.
| Item Type: | Article |
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| Uncontrolled Keywords: | Handball; Accuracy; High speed ball; Deep learning; Object detection |
| Subjects: | T Technology > T Technology (General) |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 06 Apr 2022 06:17 |
| Last Modified: | 06 Apr 2022 06:17 |
| URI: | http://umpir.ump.edu.my/id/eprint/33637 |
| Download Statistic: | View Download Statistics |
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