Beyond the Optic Lobe: Closing the Representational and Verification Gaps of STMD for Moving Target Proposal

Bio-inspired small-target motion detectors (STMDs) provide pixel-level saliency but lack proposal geometry and moving-target verification. Inspired by insect visual pathways, we present Beyond the Optic Lobe (BotL), a moving-target proposal framework compatible with seven STMD front-ends. A learning-free ON/OFF-histogram bridge converts saliency into boxes, and a binary verifier scores moving targets versus clutter without predicting categories. Under strict matching, BotL raises the area under the Recall versus false positives per image (FPPI) curve (AUC) for the default vSTMD-F front-end from 0.607 to 0.836 on re-annotated RIST and from 0.109 to 0.611 on SeaDronesSee-Tiny-SAHI (STS). Strict AUC and candidate Recall at a 15-proposal budget improve on every RIST test sequence. STS gains are large for the vSTMD family, moderate for FracSTMD, small for HaarSTMD, and negligible for ESTMD, DSTMD, and STMD+. Stage analysis distinguishes coverage constraints from verifier ranking limitations. YOLO detectors are more accurate in in-domain comparisons, but BotL leads in STS-to-RIST zero-shot transfer, with AUC 0.252 versus 0.016 and 0.030. BotL uses 16,341 learned parameters and reduces floating-point operations by a factor of 21–41 relative to these two YOLO detectors, serving as a compute-efficient proposal stage.