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

TL;DR

This paper introduces a bioinspired retinal neural network that accurately detects small moving targets in cluttered backgrounds, inspired by mammalian retina circuitry, using neurodynamics and Gabor filtering.

Contribution

It presents a novel neurodynamics-based model with multiform Gabor filtering and directional inhibition, improving small target detection in complex scenes.

Findings

Works well for targets of various sizes and velocities

Outperforms existing bioinspired models in detection accuracy

Accurately estimates motion direction and energy rapidly

Abstract

Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks. Inspired by the neural circuitry of elementary motion vision in the mammalian retina, this paper proposes a bioinspired retinal neural network based on a new neurodynamics-based temporal filtering and multiform 2-D spatial Gabor filtering. This model can estimate motion direction accurately via only two perpendicular spatiotemporal filtering signals, and respond to small targets of different sizes and velocities by adjusting the dendrite field size of the spatial filter. Meanwhile, an algorithm of directionally selective inhibition is proposed to suppress the target-like features in the moving background, which can reduce the influence of background motion effectively. Extensive synthetic and real-data experiments show that the proposed model works stably for small targets of a wider size and velocity range, and has better detection performance than other bioinspired models. Additionally, it can also extract the information of motion direction and motion energy accurately and rapidly.