M$^2$E-UAV: A Benchmark and Analysis for Onboard Motion-on-Motion Event-Based Tiny UAV Detection

Tiny UAV detection from an onboard event camera is difficult when the observer and target move at the same time. In this motion-on-motion regime, ego-motion activates background edges across buildings, vegetation, and horizon structures, while the UAV may appear as a sparse event cluster. To explore this practical problem, we present M$^2$E-UAV, a benchmark and analysis setup for onboard motion-on-motion event-based tiny UAV detection. The processed M$^2$E-UAV benchmark contains 87,223 training samples and 21,395 validation samples across four scene families: sunny building-forest, sunny farm-village, sunset building-forest, and sunset farm-village. We provide M$^2$E-Point, a point-based event baseline, and M$^2$E-Point + IMU, an IMU-conditioned variant, to analyze the role of inertial cues under onboard motion-on-motion detection. M$^2$E-Point encodes events as $[x,y,t,p]$ point sets, extracts local event structure with EdgeConv, and predicts event-level UAV foreground scores, from which bounding boxes are derived via DBSCAN. Our validation-stage analysis shows that point-based event modeling is a strong baseline, while simple IMU conditioning provides only marginal aggregate gains. Under the train/validation split, M$^2$E-Point achieves 0.9673 F1 and 0.5501 mAP50-95, while the IMU-conditioned variant reaches 0.5561 mAP50-95 with only marginal aggregate changes, serving as an initial baseline for future exploration in this domain. The dataset will be publicly released upon acceptance.