Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera
Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera
Date
2020-08
Authors
Winarno
Ali Suryaperdana Agoes
Eva Inaiyah Agustin
Deny Arifianto
Journal Title
Journal ISSN
Volume Title
Publisher
Universitas Ahmad Dahlan
Abstract
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.