Taming Dynamic Clutter: Variance-Driven Adaptive Gain Control for Bio-inspired Small Target Detection

Bio-inspired Small Target Motion Detection (STMD) mod-els often suffer “false alarm collapse” due to non-stationary environ-mental interference, failing to adapt to dynamic background statistics.We propose a bio-inspired architecture integrating macro-motion decou-pling with micro-scale Adaptive Gain Control (AGC). Macroscopically, itleverages lobula plate tangential cells (LPTC)-based population codingto eliminate ego-motion-induced baseline drifts in real-time with mini-mal overhead. Microscopically, we introduce an adaptive shunting inhi-bition operator inspired by Natural Scene Statistics (NSS). By quan-tifying local temporal variance, this mechanism maps non-stationaryclutter into a gain control factor to suppress heavy-tailed noise fromhigh-frequency flickers. This versatile plug-and-play module integratesseamlessly into existing STMD frameworks. Extensive experiments onsynthetic and real-world datasets demonstrate that our model signifi-cantly reduces false alarms while maintaining high sensitivity for dimtargets, achieving state-of-the-art precision and robustness in complexdynamic environments. Our code is publicly available at AGC.