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Robust and accurate detection of small moving targets in cluttered movingbackgrounds is a significant and challenging problem for robotic visual systemsto perform search and tracking tasks. Inspired by the neural circuitry ofelementary motion vision in the mammalian retina, this paper proposes abioinspired retinal neural network based on a new neurodynamics-based temporalfiltering and multiform 2-D spatial Gabor filtering. This model can estimatemotion direction accurately via only two perpendicular spatiotemporal filteringsignals, and respond to small targets of different sizes and velocities byadjusting the dendrite field size of the spatial filter. Meanwhile, analgorithm of directionally selective inhibition is proposed to suppress thetarget-like features in the moving background, which can reduce the influenceof background motion effectively. Extensive synthetic and real-data experimentsshow that the proposed model works stably for small targets of a wider size andvelocity range, and has better detection performance than other bioinspiredmodels. Additionally, it can also extract the information of motion directionand motion energy accurately and rapidly.

A Bioinspired Retinal Neural Network for Accurately Extracting Small-Target Motion Information in Cluttered Backgrounds