Visual Mesh: Real-Time Object Detection Using Constant Sample Density

Title: Visual Mesh: Real-Time Object Detection Using Constant Sample Density
Creator: Houliston, Trent; Chalup, Stephan K.
Relation: RoboCup 2018: Robot World Cup XXII. RoboCup 2018: Robot World Cup XXII (Quebec, Canada 18-22 June, 2018) p. 45-56
Publisher Link: http://dx.doi.org/10.1007/978-3-030-27544-0_4
Publisher: Springer
Resource Type: conference paper
Date: 2019
Description: This paper proposes an enhancement of convolutional neural networks for object detection in resource-constrained robotics through a geometric input transformation called Visual Mesh. It uses object geometry to create a graph in vision space, reducing computational complexity by normalizing the pixel and feature density of objects. The experiments compare the Visual Mesh with several other fast convolutional neural networks. The results demonstrate execution times sixteen times quicker than the fastest competitor tested, while achieving outstanding accuracy.
Subject: convolutional neural network; deep learning; ball detection; graph transformation; TensorFlow; machine vision
Identifier: http://hdl.handle.net/1959.13/1474185
Identifier: uon:49230
Identifier: ISBN:9783030275433
Identifier: ISSN:0302-9743
Language: eng
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