The developed visual system comprises three main subsystems, namely, an attention module, an STMD-based neural network, and a prediction module.
The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably, and has potential to be implemented on embedded collision detection systems for small or micro UAVs.
Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro robots that are generally limited in computational power.
The directional contrast and the extracted motion information by the motion pathway are integrated in the mushroom body for small target discrimination.
This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios.
To address this issue, we develop a new visual system model for small target motion detection against cluttered moving backgrounds.
However, if there exists a feedback loop in the STMD visual pathway or if a feedback loop could significantly improve the detection performance of STMD neurons, is unclear.
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.