The developed visual system comprises three main subsystems, namely, an attention module, an STMD-based neural network, and a prediction module.
Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro robots that are generally limited in computational power.
The presented system is a synthetic neural network, which comprises two complementary sub-systems with four spiking neurons -- the lobula giant movement detectors (LGMD1 and LGMD2) in locusts for sensing looming and recession, and the direction selective neurons (DSN-R and DSN-L) in flies for translational motion extraction.
The observed results from the experiments demonstrated that the LGMD collision detector is feasible to work as a vision module for the quadcopter's collision avoidance task.
The results demonstrated this framework is able to detect looming dark objects embedded in bright backgrounds selectively, which make it ideal for ground mobile platforms.