College of Big Data and Information Engineering, Guizhou University, Guizhou Provincial Characteristic Key Laboratory of System Optimization and Scientific Computing, Guiyang, Guizhou 550025, PR China.
College of Big Data and Information Engineering, Guizhou University, Guizhou Provincial Characteristic Key Laboratory of System Optimization and Scientific Computing, Guiyang, Guizhou 550025, PR China.
Neural Netw. 2021 Mar;135:13-28. doi: 10.1016/j.neunet.2020.11.018. Epub 2020 Dec 5.
The biological visual system includes multiple types of motion sensitive neurons which preferentially respond to specific perceptual regions. However, it still keeps open how to borrow such neurons to construct bio-inspired computational models for multiple-regional collision detection. To fill this gap, this work proposes a visual joint perception neural network with two subnetworks - presynaptic and postsynaptic neural networks, inspired by the preferentialperception characteristics of three horizontal and vertical motion sensitive neurons. Related to the neural network and three hazard detection mechanisms, an artificial fly visual synthesized collision detection model for multiple-regional collision detection is originally developed to monitor possible danger occurrence in the case where one or more moving objects appear in the whole field of view. The experiments can clearly draw two conclusions: (i) the acquired neural network can effectively display the characteristics of visual movement, and (ii) the collision detection model, which outperforms the compared models, can effectively perform multiple-regional collision detection at a high success rate, and only takes about 0.24s to complete the process of collision detection for each virtual or actual image frame with resolution 110×60.
生物视觉系统包括多种类型的运动敏感神经元，它们优先对特定的感知区域做出反应。然而，如何借用这些神经元来构建用于多区域碰撞检测的仿生计算模型仍然存在问题。为了填补这一空白，本工作提出了一种具有两个子网的视觉联合感知神经网络——前突触和后突触神经网络，灵感来自于三种水平和垂直运动敏感神经元的优先感知特性。与神经网络和三种危险检测机制相关，我们最初开发了一种用于多区域碰撞检测的人工苍蝇视觉综合碰撞检测模型，以监测在整个视野中出现一个或多个移动对象的情况下可能发生的危险。实验可以清楚地得出两个结论：（i）获得的神经网络可以有效地显示视觉运动的特征；（ii）碰撞检测模型在高成功率下有效地执行多区域碰撞检测，并且仅需约 0.24s 即可完成分辨率为 110×60 的每个虚拟或实际图像帧的碰撞检测过程。
