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 preferential perception 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$\times$60.

生物视觉系统包括多种类型的运动敏感神经元，它们优先响应特定的感知区域。但是，如何借用这种神经元来构建生物启发的用于多区域碰撞检测的计算模型仍然是一个开放的话题。为了填补这一空白，这项工作提出了一个视觉联合感知神经网络，该网络具有两个子网络-突触前和突触后神经网络，其灵感来自于三个水平和垂直运动敏感神经元的优先感知特性。涉及神经网络和三种危害检测机制，最初是一种用于多区域碰撞检测的人工蝇视觉综合碰撞检测模型。开发用于监视在整个视野中出现一个或多个移动物体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）所获得的神经网络可以有效地显示视觉运动的特征；（ii）优于检测模型的碰撞检测模型，可以在高效率下有效地执行多区域碰撞检测。成功率，并且只需要大约0.24s即可完成分辨率为110的每个虚拟或实际图像帧的碰撞检测过程$\times$60