Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

TL;DR

This review discusses computational models inspired by insect visual systems for motion perception, highlighting biological mechanisms, neural network models, and their applications in robotics and vehicles, emphasizing the integration and hardware realization.

Contribution

It provides a comprehensive overview of biologically inspired motion perception models from insects and their applications, summarizing methodologies and discussing system integration and hardware implementation.

Findings

Neural models include LGMDs, DSNs, and STMDs.

Models effectively simulate biological motion detection.

Applications extend to robotics and autonomous vehicles.

Abstract

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modelling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research of insects' visual systems in the literature. These motion perception models or neural networks comprise the looming sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation sensitive neural systems of direction selective neurons (DSNs) in fruit flies, bees and locusts, as well as the small target motion detectors (STMDs) in dragonflies and hover flies. We also review the applications of these models to robots and vehicles. Through these modelling studies, we summarise the methodologies that generate different direction and size selectivity in motion perception. At last, we discuss about multiple systems integration and hardware realisation of these bio-inspired motion perception models.