Ling Jun, Wang Hongxin, Xu Mingshuo, Chen Hao, Li Haiyang, Peng Jigen
School of Mathematics and Information Science, Guangzhou University, Guangzhou, China.
Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou, China.
Front Neurorobot. 2022 Sep 20;16:984430. doi: 10.3389/fnbot.2022.984430. eCollection 2022.
Building an efficient and reliable small target motion detection visual system is challenging for artificial intelligence robotics because a small target only occupies few pixels and hardly displays visual features in images. Biological visual systems that have evolved over millions of years could be ideal templates for designing artificial visual systems. Insects benefit from a class of specialized neurons, called small target motion detectors (STMDs), which endow them with an excellent ability to detect small moving targets against a cluttered dynamic environment. Some bio-inspired models featured in feed-forward information processing architectures have been proposed to imitate the functions of the STMD neurons. However, feedback, a crucial mechanism for visual system regulation, has not been investigated deeply in the STMD-based neural circuits and its roles in small target motion detection remain unclear. In this paper, we propose a time-delay feedback STMD model for small target motion detection in complex backgrounds. The main contributions of this study are as follows. First, a feedback pathway is designed by transmitting information from output-layer neurons to lower-layer interneurons in the STMD pathway and the role of the feedback is analyzed from the view of mathematical analysis. Second, to estimate the feedback constant, the existence and uniqueness of solutions for nonlinear dynamical systems formed by feedback loop are analyzed Schauder's fixed point theorem and contraction mapping theorem. Finally, an iterative algorithm is designed to solve the nonlinear problem and the performance of the proposed model is tested by experiments. Experimental results demonstrate that the feedback is able to weaken background false positives while maintaining a minor effect on small targets. It outperforms existing STMD-based models regarding the accuracy of fast-moving small target detection in visual clutter. The proposed feedback approach could inspire the relevant modeling of robust motion perception robotics visual systems.
构建一个高效可靠的小目标运动检测视觉系统对人工智能机器人技术来说具有挑战性，因为小目标只占据很少的像素，并且在图像中几乎不显示视觉特征。经过数百万年进化而来的生物视觉系统可能是设计人工视觉系统的理想模板。昆虫受益于一类特殊的神经元，称为小目标运动检测器（STMD），这使它们具有在杂乱的动态环境中检测小移动目标的出色能力。已经提出了一些具有前馈信息处理架构的仿生模型来模仿STMD神经元的功能。然而，反馈作为视觉系统调节的关键机制，在基于STMD的神经回路中尚未得到深入研究，其在小目标运动检测中的作用仍不清楚。在本文中，我们提出了一种用于复杂背景下小目标运动检测的时延反馈STMD模型。本研究的主要贡献如下。首先，通过在STMD通路中从输出层神经元向下层中间神经元传输信息来设计反馈通路，并从数学分析的角度分析反馈的作用。其次，为了估计反馈常数，利用绍德尔不动点定理和压缩映射定理分析了由反馈回路形成的非线性动力系统解的存在性和唯一性。最后，设计了一种迭代算法来解决非线性问题，并通过实验测试了所提出模型的性能。实验结果表明，反馈能够减弱背景误报，同时对小目标的影响较小。在视觉杂波中快速移动小目标检测的准确性方面，它优于现有的基于STMD的模型。所提出的反馈方法可能会启发鲁棒运动感知机器人视觉系统的相关建模。
