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 paper, 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 it shows 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.
翻译:复杂视觉环境中的小型移动物体的高度敏感性,对于计算能力通常有限的自主微型机器人来说,是一个巨大的挑战。通过利用其高度发展的视觉系统,飞虫能够有效地探测伴侣,在快速追逐中跟踪猎物,即使小目标只相当于其视觉场中的几像素。对小目标移动的高度敏感性得到一组专门神经元的辅助,这些神经元称为小型目标运动探测器(STMDs),现有的STMD计算模型通常包括四种依次排列的神经层,通过向前回路连接,从原始视觉输入中提取关于小目标运动的信息。然而,反馈是另一个运动感知的重要监管圈,但在STMD路径上还没有被调查,其小目标运动探测的功能也不清楚。在本文中,我们建议基于STMD的神经网络与反馈连接(Feedback STMDMD), 网络输出时间被延迟,然后反馈到更低的层层,然后反馈到更低层。我们把模型的特性与不延时回回回回回,我们发现它更偏向高速度目标。在高速度目标上显示高速度的直观,同时显示高速度的图像显示。在快速的图像上显示。