Ball Classification through Object Detection using Deep Learning for Handball
Keywords:Handball, Accuracy, High Speed Ball, Deep Learning, Object Detection
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.
How to Cite
Copyright (c) 2020 Arzielah Ashiqin Alwi, Ahmad Najmuddin Ibrahim, Muhammad Nur Aiman Shapiee, Muhammad Ar Rahim Ibrahim, Mohd Azraai Mohd Razman, Ismail Mohd Khairuddin
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.