基于膨胀焦点的生物启发碰撞检测神经网络

doi:10.19678/j.issn.1000-3428.0252184

摘要/Abstract

摘要： 利用传统计算机视觉技术来解决复杂场景中的碰撞检测问题是一项十分艰巨的任务，尤其是面临虚碰撞干扰时模型误检率较高而准确率较低。针对该问题，本文基于哺乳动物视网膜分层结构特性，借助灵长类动物大脑皮层中央前回多感觉皮质区（PZ）神经元对特定视野区的危险感知特性，提出了一种能有效降低复杂场景中虚碰撞干扰的生物启发式增强碰撞检测神经网络（ECDNN）。该网络包含突触前和突触后两部分神经子网络结构。突触前子网络基于哺乳动物视网膜信息分层处理，逐级传递特性，从低阶视觉信息感知全局膨胀焦点（FOE）来划分动态聚焦感受野，以此获取关键视觉信息。突触后子网络整合聚焦感受野内由迫近视觉刺激引发的膜电位兴奋响应，输出表征迫近碰撞危险的警报信号。实验结果表明，该模型不仅能有效过滤复杂场景中虚碰撞干扰，降低模型误检，还将碰撞检测准确率提升至96%以上，可为构建人工智能交互系统提供重要基础。

关键词: 碰撞检测, 虚碰撞, 膨胀焦点, 迫近危险感知, 膜电位兴奋, 生物视觉

Abstract: Using traditional computer vision technology to solve collision detection problems in complex scenes is extremely difficult, particularly when dealing with false collision interference, where the model exhibits a high false alarm rate and low accuracy. To address this problem, this paper proposes a bio-inspired Enhanced Collision Detection Neural Network (ECDNN) that effectively reduces false collision interference in complex scenes. This network is developed based on the hierarchical structural characteristics of the mammalian retina by leveraging the danger perception properties of neurons in the Polysensory Zone (PZ) of the precentral gyrus in the primate cerebral cortex toward specific visual field regions. The network consists of two parts: a presynaptic neural subnetwork and a postsynaptic neural subnetwork. Specifically, the presynaptic subnetwork, which is based on the hierarchical processing and stepwise transmission characteristics of mammalian retinal information, perceives the global Focus of Expansion (FOE) from low-level visual information to divide the dynamic focus receptive field, thereby obtaining key visual information. The postsynaptic subnetwork integrates the membrane potential excitatory responses elicited by approaching visual stimuli within the focus-receptive field and outputs an alarm signal representing imminent collision danger. Experimental results show that the proposed model can effectively filter false collision interference in complex scenes, reduce false detections, and improve collision detection accuracy to over 96%. The study findings provide an important foundation for building future artificial intelligence interactive systems.

Key words: collision detection, false collision, Focus of Expansion (FOE), looming danger perception, membrane potential excitation, biological vision

中图分类号:

TP391

皮成东, 胡滨. 基于膨胀焦点的生物启发碰撞检测神经网络[J]. 计算机工程, 2026, 52(11): 219-238.

PI Chengdong, HU Bin. Bio-Inspired Neural Network for Collision Detection Based on Focus of Expansion[J]. Computer Engineering, 2026, 52(11): 219-238.

https://www.ecice06.com/CN/Y2026/V52/I11/219

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