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Nurhadi Nurhadi
University of Muhamadiyah Malang
Computer Science & IT Electrical & Electronics Engineering

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Object Detection of Omnidirectional Vision Using PSO-Neural Network for Soccer Robot Novendra Setyawan; Nuralif Mardiyah; Khusnul Hidayat; Nurhadi Nurhadi; Zulfatman Has
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.1 KB) | DOI: 10.11591/eecsi.v5.1696

Abstract

The vision system in soccer robot is needed to recognize the object around the robot environment. Omnidirectional vision system has been widely developed to find the object such as a ball, goalpost, and the white line in a field and recognized the distance and an angle between the object and robot. The most challenging in develop Omni-vision system is image distortion resulting from spherical mirror or lenses. This paper presents an efficient Omni-vision system using spherical lenses for real-time object detection. Aiming to overcome the image distortion and computation complexity, the distance calculation between object and robot from the spherical image is modeled using the neural network with optimized by particle swarm optimization. The experimental result shows the effectiveness of our development in the term of accuracy and processing time.
Co-Authors Khusnul Hidayat Novendra Setyawan Nuralif Mardiyah Zulfatman Zulfatman