Systematic Evaluation of Biologically Inspired Motion Detection Models: From LGMD and EMD to Hybrid Spiking Neural Networks

Collision detection in dynamic environments demands perception systems that are both computationally efficient and robust to diverse motion patterns. Biological vision systems, particularly those of insects, offer efficient neural architectures capable of rapid motion interpretation under strict resource constraints. This work presents a systematic comparative evaluation of three biologically inspired models: the Lobula Giant Movement Detector (LGMD), the Elementary Motion Detector (EMD), and a hybrid Spiking Neural Network (SNN) incorporating LGMD and EMD-derived motion processing pathways, evaluated on programmatically generated synthetic stimuli with frame-level ground truth. The hybrid SNN achieved an accuracy of 73–87% across stimulus types, consistently exceeding the 75.0% held-out test set baseline, with a precision of 1.0 throughout and a substantially lower runtime than the LGMD implementation. LGMD demonstrated rate-based sensitivity consistent with biological spike-frequency adaptation, while the EMD correctly produced near-zero responses to looming stimuli, confirming its role as a directional rather than collision detector. These results demonstrate that hybridising biologically inspired motion detectors within a trainable spiking framework produces a promising and reproducible approach to collision prediction, while identifying the sim-to-real generalisation gap as a key challenge for future deployment.