Path: Top -> Journal -> Telkomnika -> 2020 -> Vol 18, No 4, August
Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera
By : Winarno Winarno, Ali Suryaperdana Agoes, Eva Inaiyah Agustin, Deny Arifianto, Telkomnika
Created : 2021-01-18, with 1 files
Keyword : deep learning; object detection; robot soccer;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/15009
Document Source : web
Kontes Robot Sepak Bola Indonesia (KRSBI) is an annual event for contestants to compete their design and robot engineering in the field of robot soccer. Each contestant tries to win the match by scoring a goal toward the opponent's goal. In order to score a goal, the robot needs to find the ball, locate the goal, then kick the ball toward goal. We employed an omnidirectional vision camera as a visual sensor for a robot to perceive the objects information. We calibrated streaming images from the camera to remove the mirror distortion. Furthermore, we deployed PeleeNet as our deep learning model for object detection. We fine-tuned PeleeNet on our dataset generated from our image collection. Our experiment result showed PeleeNet had the potential for deep learning mobile platform in KRSBI as the object detection architecture. It had a perfect combination of memory efficiency, speed and accuracy.
Property | Value |
---|---|
Publisher ID | gdlhub |
Organization | Telkomnika |
Contact Name | Herti Yani, S.Kom |
Address | Jln. Jenderal Sudirman |
City | Jambi |
Region | Jambi |
Country | Indonesia |
Phone | 0741-35095 |
Fax | 0741-35093 |
Administrator E-mail | elibrarystikom@gmail.com |
CKO E-mail | elibrarystikom@gmail.com |
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