AI RESEARCH
LER-YOLO: Reliability-Aware Expert Routing for Misaligned RGB-Infrared UAV Detection
arXiv CS.CV
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ArXi:2605.20667v1 Announce Type: new Detecting small unmanned aerial vehicles from RGB-infrared remote-sensing pairs remains challenging due to tiny target scale, cluttered backgrounds, and spatial misalignment between heterogeneous sensors. Existing bimodal detectors often align or fuse features without assessing the reliability of local cross-sensor correspondence, allowing mismatch artifacts to propagate into the detection head. To address this issue, we propose LER-YOLO, a reliability-aware sparse mixture-of-experts framework for misaligned RGB-infrared UAV detection. LER-YOLO first.