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

Fu, Qinbing and Yue, Shigang and Hu, Cheng and Peng, Jigen and Rind, Claire (2019) A Robust Collision Perception Visual Neural Network with Specific Selectivity to Darker Objects. IEEE Transactions on Cybernetics . ISSN 2168-2267

Full content URL: http://doi.org/10.1109/TCYB.2019.2946090

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A Robust Collision Perception Visual Neural Network with Specific Selectivity to Darker Objects
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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, i.e. 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 facing with. However, little has been done on modelling the LGMD2 and investigating its potential in robotics and vehicles. In this research, we build an LGMD2 visual neural network which possesses the similar collision selectivity of an LGMD2 neuron in locust, via the modelling 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.

Keywords:LGMD2, neuron model, darker objects selectivity, collision perception, ON and OFF pathways, mobile robots
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:37664
Deposited On:08 Oct 2019 09:21

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