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A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects

Lookup NU author(s): Dr Claire Rind

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2020.

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Abstract

Building an efficient and reliable collision perception visual system is a challenging problem for future robots and autonomous vehicles. The biological visual neural networks, which have evolved over millions of years in nature and are working perfectly in the real world, could be ideal models for designing artificial vision systems. In the locust’s visual pathways, a lobula giant movement detector (LGMD), that is, the LGMD2, has been identified as a looming perception neuron that responds most strongly to darker approaching objects relative to their backgrounds; similar situations which many ground vehicles and robots are often faced with. However, little has been done on modeling the LGMD2 and investigating its potential in robotics and vehicles. In this article, we build an LGMD2 visual neural network which possesses the similar collision selectivity of an LGMD2 neuron in locust via the modeling of biased-ON and –OFF pathways splitting visual signals into parallel ON/OFF channels. With stronger inhibition (bias) in the ON pathway, this model responds selectively to darker looming objects. The proposed model has been tested systematically with a range of stimuli including real-world scenarios. It has also been implemented in a micro-mobile robot and tested with real-time experiments. The experimental results have verified the effectiveness and robustness of the proposed model for detecting darker looming objects against various dynamic and cluttered backgrounds.


Publication metadata

Author(s): Fu Q, Hu C, Peng J, Rind FC, Yue S

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Cybernetics

Year: 2020

Pages: epub ahead of print

Online publication date: 04/12/2019

Acceptance date: 18/09/2019

Date deposited: 28/01/2020

ISSN (print): 2168-2267

ISSN (electronic): 2168-2275

Publisher: IEEE

URL: https://doi.org/10.1109/TCYB.2019.2946090

DOI: 10.1109/TCYB.2019.2946090


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