Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro-robots that are generally limited in computational power. By exploiting their highly evolved visual systems, flying insects can effectively detect mates and track prey during rapid pursuits, even though the small targets equate to only a few pixels in their visual field. The high degree of sensitivity to small target movement is supported by a class of specialized neurons called small target motion detectors (STMDs). Existing STMD-based computational models normally comprise four sequentially arranged neural layers interconnected via feedforward loops to extract information on small target motion from raw visual inputs. However, feedback, another important regulatory circuit for motion perception, has not been investigated in the STMD pathway and its functional roles for small target motion detection are not clear. In this article, we propose an STMD-based neural network with feedback connection (feedback STMD), where the network output is temporally delayed, then fed back to the lower layers to mediate neural responses. We compare the properties of the model with and without the time-delay feedback loop and find that it shows a preference for high-velocity objects. Extensive experiments suggest that the feedback STMD achieves superior detection performance for fast-moving small targets, while significantly suppressing background false positive movements which display lower velocities. The proposed feedback model provides an effective solution in robotic visual systems for detecting fast-moving small targets that are always salient and potentially threatening.

中文翻译：

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用于区分复杂动态环境中小型快速移动目标的时滞反馈神经网络


对于计算能力通常有限的自主微型机器人来说，区分复杂视觉环境中的小型移动物体是一个重大挑战。通过利用高度进化的视觉系统，飞行昆虫可以在快速追击过程中有效地发现配偶并跟踪猎物，即使小目标仅相当于其视野中的几个像素。对小目标运动的高度敏感性由一类称为小目标运动检测器（STMD）的专门神经元支持。现有的基于 STMD 的计算模型通常包含四个顺序排列的神经层，这些神经层通过前馈循环互连，以从原始视觉输入中提取有关小目标运动的信息。然而，反馈作为运动感知的另一个重要调节回路，尚未在 STMD 通路中得到研究，其在小目标运动检测中的功能作用尚不清楚。在本文中，我们提出了一种基于 STMD 的具有反馈连接的神经网络（反馈 STMD），其中网络输出被暂时延迟，然后反馈到较低层以调节神经响应。我们比较了有和没有时滞反馈回路的模型的属性，发现它显示出对高速物体的偏好。大量实验表明，反馈 STMD 对快速移动的小目标实现了卓越的检测性能，同时显着抑制了速度较低的背景误报运动。所提出的反馈模型为机器人视觉系统提供了一种有效的解决方案，用于检测始终显着且具有潜在威胁的快速移动的小目标。