Tri-Modal Fusion Transformers for UAV-based Object Detection

• URL: http://arxiv.org/abs/2604.16630v1
• Date: Fri, 17 Apr 2026 18:35:06 GMT
• Title: Tri-Modal Fusion Transformers for UAV-based Object Detection
• Abstract summary: We present a tri-modal framework that processes RGB, thermal, and event data with a dual-stream hierarchical vision transformer.<n>We introduce a 10,489-frame UAV dataset with synchronized and pre-aligned RGB-thermal-event streams and 24,223 annotated vehicles.
• Score: 1.338174941551702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable UAV object detection requires robustness to illumination changes, motion blur, and scene dynamics that suppress RGB cues. Thermal long-wave infrared (LWIR) sensing preserves contrast in low light, and event cameras retain microsecond-level temporal edges, but integrating all three modalities in a unified detector has not been systematically studied. We present a tri-modal framework that processes RGB, thermal, and event data with a dual-stream hierarchical vision transformer. At selected encoder depths, a Modality-Aware Gated Exchange (MAGE) applies inter-sensor channel and spatial gating, and a Bidirectional Token Exchange (BiTE) module performs bidirectional token-level attention with depthwise-pointwise refinement, producing resolution-preserving fused maps for a standard feature pyramid and two-stage detector. We introduce a 10,489-frame UAV dataset with synchronized and pre-aligned RGB-thermal-event streams and 24,223 annotated vehicles across day and night flights. Through 61 controlled ablations, we evaluate fusion placement, mechanism (baseline MAGE+BiTE, CSSA, GAFF), modality subsets, and backbone capacity. Tri-modal fusion improves over all dual-modal baselines, with fusion depth having a significant effect and a lightweight CSSA variant recovering most of the benefit at minimal cost. This work provides the first systematic benchmark and modular backbone for tri-modal UAV-based object detection.

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