Version 1
: Received: 26 November 2021 / Approved: 29 November 2021 / Online: 29 November 2021 (12:28:15 CET)
How to cite:
Sokolinsky, L.B.; Starkov, A.E. Building 2D Model of Compound Eye Vision for Machine Learning. Preprints2021, 2021110530. https://doi.org/10.20944/preprints202111.0530.v1
Sokolinsky, L.B.; Starkov, A.E. Building 2D Model of Compound Eye Vision for Machine Learning. Preprints 2021, 2021110530. https://doi.org/10.20944/preprints202111.0530.v1
Sokolinsky, L.B.; Starkov, A.E. Building 2D Model of Compound Eye Vision for Machine Learning. Preprints2021, 2021110530. https://doi.org/10.20944/preprints202111.0530.v1
APA Style
Sokolinsky, L.B., & Starkov, A.E. (2021). Building 2D Model of Compound Eye Vision for Machine Learning. Preprints. https://doi.org/10.20944/preprints202111.0530.v1
Chicago/Turabian Style
Sokolinsky, L.B. and Artem E. Starkov. 2021 "Building 2D Model of Compound Eye Vision for Machine Learning" Preprints. https://doi.org/10.20944/preprints202111.0530.v1
Abstract
This paper presents a two-dimensional mathematical model of compound eye vision. Such a model is useful for solving navigation issues for autonomous mobile robots on the ground plane. The model is inspired by the insect compound eye that consists of ommatidia, which are tiny independent photoreception units, each of which combines a cornea, lens, and rhabdom. The model describes the planar binocular compound eye vision, focusing on measuring distance and azimuth to a circular feature with an arbitrary size. The model provides a necessary and sufficient condition for the visibility of a circular feature by each ommatidium. On this basis, an algorithm is built for generating a training data set to create two deep neural networks (DNN): the first detects the distance, and the second detects the azimuth to a circular feature. The hyperparameter tuning and the configurations of both networks are described. Experimental results showed that the proposed method could effectively and accurately detect the distance and azimuth to objects.
Keywords
robot vision; compound eye; two-dimensional model; distance measurement; azimuth measurement; deep learning; training data set generation; deep neural network
Subject
Computer Science and Mathematics, Robotics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
