Looming Detection in Complex Dynamic Visual Scenes by Interneuronal Coordination of Motion and Feature Pathways

Detecting looming signals for collision avoidance encounters challenges in real-world scenarios, where moving backgrounds can interfere as an agent navigates through complex natural environments. Remarkably, even insects with limited neural systems adeptly respond to looming stimuli while in motion at high speeds. Existing insect-inspired looming detection models typically rely on either motion-pathway or feature-pathway signals, yet both are susceptible to dynamic visual scene interference. Coordinating interneuron signals from both pathways can enhance the looming detection performance under dynamic conditions. An artificial neural network is employed to construct a combined-pathway model based on Drosophila anatomy. The model outperforms state-of-the-art bio-inspired looming-detection models in tasks involving dynamic backgrounds, simulated by animated 2D-moving natural scenes or recorded in reality when an unmanned aerial vehicle performs obstacle collision avoidance tasks. Notably, by combining neural anatomy architecture and appropriate multiobjective tasks, the model exhibits convergent neural dynamics with biological counterparts post-training, offering network explanations and mechanistic insights. Specifically, a multiplicative interneuron operation enhances looming signal patterns and reduces background interferences, generalizing to more complex scenarios, such as AirSim 3D environments and real-world situations. The work introduces testable biological hypotheses and a promising bioinspired solution for looming detection in dynamic visual environments.