  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 fly'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 directional selectivity which means these STMDs respond strongly
only to their preferred motion direction. Directional 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 directional selective STMD neurons. In this
paper, we propose a directional selective STMD-based neural network (DSTMD) 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 STMD
neurons. 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 small targets against
cluttered backgrounds.
