TELKOMNIKA Telecommunication, Computing, Electronics and Control
Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera
Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera
Subject
Deep learning, Object detection, Robot soccer
Description
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 object’s 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.
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 object’s 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.
Creator
Winarno, Ali Suryaperdana Agoes, Eva Inaiyah Agustin, Deny Arifianto
Source
DOI: 10.12928/TELKOMNIKA.v18i4.15009
Publisher
Universitas Ahmad Dahlan
Date
August 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Winarno, Ali Suryaperdana Agoes, Eva Inaiyah Agustin, Deny Arifianto, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4011.
Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4011.