Guangzhou University, School of Mechanical and Electrical Engineering; Machine Life and Intelligence Research Centre.
University of Lincoln, Computational Intelligence Lab, School of Computer Science; Lincoln Centre for Autonomous Systems.
Artif Life. 2019 Summer;25(3):263-311. doi: 10.1162/artl_a_00297.
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.
运动感知是决定昆虫生活多方面的关键能力，包括躲避捕食者、觅食等。在昆虫的视觉通路上已经发现了大量的运动探测器。这些运动探测器的计算建模不仅为人工智能提供了有效解决方案，也有助于理解复杂的生物视觉系统。这些经过数百万年进化发展的生物机制，将为未来智能机器的动态视觉系统构建提供坚实的模块。本文综述了文献中源自昆虫视觉系统生物研究的计算运动感知模型。这些运动感知模型或神经网络包括蝗虫中巨大运动探测器（LGMDs）的逼近敏感神经元模型、果蝇、蜜蜂和蝗虫中的方向选择性神经元（DSNs）的平移敏感神经系统，以及蜻蜓和食蚜蝇的小目标运动探测器（STMDs）。我们还回顾了这些模型在机器人和车辆中的应用。通过这些建模研究，我们总结了产生运动感知中不同方向和大小选择性的方法。最后，我们讨论了这些生物启发的运动感知模型的多系统集成和硬件实现。
