  Small target motion detection within complex natural environments is an
extremely challenging task for autonomous robots. Surprisingly, the visual
systems of insects have evolved to be highly efficient in detecting mates and
tracking prey, even though targets occupy as small as a few degrees of their
visual fields. The excellent sensitivity to small target motion relies on a
class of specialized neurons called small target motion detectors (STMDs).
However, existing STMD-based models are heavily dependent on visual contrast
and perform poorly in complex natural environments where small targets
generally exhibit extremely low contrast against neighbouring backgrounds. In
this paper, we develop an attention and prediction guided visual system to
overcome this limitation. The developed visual system comprises three main
subsystems, namely, an attention module, an STMD-based neural network, and a
prediction module. The attention module searches for potential small targets in
the predicted areas of the input image and enhances their contrast against
complex background. The STMD-based neural network receives the
contrast-enhanced image and discriminates small moving targets from background
false positives. The prediction module foresees future positions of the
detected targets and generates a prediction map for the attention module. The
three subsystems are connected in a recurrent architecture allowing information
to be processed sequentially to activate specific areas for small target
detection. Extensive experiments on synthetic and real-world datasets
demonstrate the effectiveness and superiority of the proposed visual system for
detecting small, low-contrast moving targets against complex natural
environments.
