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 modeling 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 on insects' visual systems in the literature. These motion perception models or neural networks consist of 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, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

Reliably recognizing objects approaching on a collision course is extremely important. A synthetic vision system is proposed to tackle the problem of collision recognition in dynamic environments. The system combines the outputs of four whole-field motion-detecting neurons, each receiving inputs from a network of neurons employing asymmetric lateral inhibition to suppress their responses to one direction of motion. An evolutionary algorithm is then used to adjust the weights between the four motion-detecting neurons to tune the system to detect collisions in two test environments. To do this, a population of agents, each representing a proposed synthetic visual system, either were shown images generated by a mobile Khepera robot navigating in a simplified laboratory environment or were shown images videoed outdoors from a moving vehicle. The agents had to cope with the local environment correctly in order to survive. After 400 generations, the best agent recognized imminent collisions reliably in the familiar environment where it had evolved. However, when the environment was swapped, only the agent evolved to cope in the robotic environment still signaled collision reliably. This study suggests that whole-field direction-selective neurons, with selectivity based on asymmetric lateral inhibition, can be organized into a synthetic vision system, which can then be adapted to play an important role in collision detection in complex dynamic scenes.