  Looming detection plays an important role in insect collision prevention
systems. As a vital capability evolutionary survival, it has been extensively
studied in neuroscience and is attracting increasing research interest in
robotics due to its close relationship with collision detection and navigation.
Visual cues such as angular size, angular velocity, and expansion have been
widely studied for looming detection by means of optic flow or elementary
neural computing research. However, a critical visual motion cue has been long
neglected because it is so easy to be confused with expansion, that is
radial-opponent-motion (ROM). Recent research on the discovery of LPLC2, a
ROM-sensitive neuron in Drosophila, has revealed its ultra-selectivity because
it only responds to stimuli with focal, outward movement. This characteristic
of ROM-sensitivity is consistent with the demand for collision detection
because it is strongly associated with danger looming that is moving towards
the center of the observer. Thus, we hope to extend the well-studied neural
model of the lobula giant movement detector (LGMD) with ROM-sensibility in
order to enhance robustness and accuracy at the same time. In this paper, we
investigate the potential to extend an image velocity-based looming detector,
the lobula giant movement detector (LGMD), with ROM-sensibility. To achieve
this, we propose the mathematical definition of ROM and its main property, the
radial motion opponency (RMO). Then, a synaptic neuropile that analogizes the
synaptic processing of LPLC2 is proposed in the form of lateral inhibition and
attention. Thus, our proposed model is the first to perform both image velocity
selectivity and ROM sensitivity. Systematic experiments are conducted to
exhibit the huge potential of the proposed bio-inspired looming detector.
