IEEE Trans Cybern. 2020 Apr;50(4):1541-1555. doi: 10.1109/TCYB.2018.2869384. Epub 2018 Oct 8.
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.
在杂乱背景下识别运动目标极具挑战性，更不用说检测小至一个或几个像素的目标并对其进行跟踪了。在昆虫的视觉系统中，已经确定了一类特定的神经元，称为小目标运动检测器 (STMD)，它们对小目标运动具有极高的选择性。其中一些 STMD 还表现出方向选择性，这意味着这些 STMD 仅对其首选运动方向做出强烈响应。方向选择性是这些 STMD 神经元的一个重要特性，它有助于跟踪小目标，例如飞行中的伴侣。然而，在系统地对这些具有方向选择性的 STMD 神经元进行建模方面，研究还很少。在本文中，我们提出了一种基于具有方向选择性的 STMD 的神经网络，用于在杂乱背景下检测小目标。在提出的神经网络中，通过对来自两个像素的信号进行相关，引入了一种新的相关机制来实现方向选择性。然后，在空间域上实现侧抑制机制，以实现 STMD 神经元的大小选择性。最后，使用群体向量算法对小目标的运动方向进行编码。大量实验表明，该神经网络不仅符合当前的生物学发现，即表现出方向偏好，而且还能够可靠地检测杂乱背景中的小目标。
