Discriminating targets moving against a cluttered background is a huge challenge, let alone detecting a target as small as one or a few pixels and tracking it in flight. In the insect’s visual system, a class of specific neurons, called small target motion detectors (STMDs), have been identified as showing exquisite selectivity for small target motion. Some of the STMDs have also demonstrated direction selectivity which means these STMDs respond strongly only to their preferred motion direction. Direction selectivity is an important property of these STMD neurons which could contribute to tracking small targets such as mates in flight. However, little has been done on systematically modeling these directionally selective STMD neurons. In this paper, we propose a directionally selective STMD-based neural network for small target detection in a cluttered background. In the proposed neural network, a new correlation mechanism is introduced for direction selectivity via correlating signals relayed from two pixels. Then, a lateral inhibition mechanism is implemented on the spatial field for size selectivity of the STMD neurons. Finally, a population vector algorithm is used to encode motion direction of small targets. Extensive experiments showed that the proposed neural network not only is in accord with current biological findings, i.e., showing directional preferences but also worked reliably in detecting the small targets against cluttered backgrounds.

中文翻译：

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杂乱背景下的定向选择性小目标运动检测视觉神经网络

区分在混乱背景下移动的目标是一个巨大的挑战，更不用说检测到一个或几个像素的目标并在飞行中对其进行跟踪了。在昆虫的视觉系统中，一类称为小目标运动检测器（STMD）的特定神经元已被识别为对小目标运动表现出出色的选择性。一些STMD还显示了方向选择性，这意味着这些STMD仅对其首选的运动方向有强烈的响应。方向选择性是这些STMD神经元的重要属性，可能有助于跟踪飞行中的伴侣等小目标。但是，在系统地建模这些方向选择性的STMD神经元方面，几乎没有做过任何事情。在本文中，我们提出在杂乱背景下用于小目标检测的基于方向选择性STMD的神经网络。在提出的神经网络中，引入了一种新的相关机制，用于通过关联从两个像素中继的信号来进行方向选择性。然后，在空间场上实施横向抑制机制，以实现STMD神经元的大小选择性。最后，使用种群矢量算法对小目标的运动方向进行编码。大量实验表明，所提出的神经网络不仅符合当前的生物学发现，即显示出方向性偏好，而且在检测杂乱背景下的小目标时也能可靠地工作。引入了一种新的相关机制，用于通过从两个像素中继来的相关信号进行方向选择性。然后，在空间场上实施横向抑制机制，以实现STMD神经元的大小选择性。最后，使用种群矢量算法对小目标的运动方向进行编码。大量实验表明，所提出的神经网络不仅符合当前的生物学发现，即显示出方向性偏好，而且在检测杂乱背景下的小目标时也能可靠地工作。引入了一种新的相关机制，用于通过从两个像素中继来的相关信号进行方向选择性。然后，在空间场上实施横向抑制机制，以实现STMD神经元的大小选择性。最后，使用种群矢量算法对小目标的运动方向进行编码。大量实验表明，所提出的神经网络不仅符合当前的生物学发现，即显示出方向性偏好，而且在检测杂乱背景下的小目标时也能可靠地工作。