Enhanced LGMD Model with Adaptive Probabilistic Regulation for Compound Interference

When subjected to compound interference, such as spatial noise and high-frequency jitter, current LGMD-inspired collision detection models for micro-robots are prone to false alarms and perceptual degradation. To address this challenge, this paper proposes an enhanced visual perception model that incorporates adaptive Gaussian random variables and spatial residual feedback (SRF). These random variables filter out discrete spatial noise, while the SRF suppresses global image shifts induced by jitter. Evaluations on synthetic and real-world video sequences validate the proposed mechanisms. Comparative results demonstrate that the model effectively reduces false responses under compound interference, thereby maintaining robust success rate (SR), discrimination ratio (DR), and membrane potential stability index (MPSI) metrics. To explain this robustness, ablation analyses further verify the synergistic benefits of the SRF and the Gaussian random variables. Furthermore, statistical results on the random variables indicate that, under compound interference, the adaptive probabilistic model outperforms fixed probabilistic configurations. By ensuring robust collision perception against such interference, this work enhances the practical viability of LGMD-inspired visual systems.