QA²FDet: Quality-Aware Adaptive Alignment Fusion Network for UAV RGBT Tiny Pedestrian Detection

Visible-thermal tiny pedestrian detection in UAV aerial images is crucial for online decision-making in urban security and disaster response. However, the extremely small scale and sparse distribution of pedestrians cause discriminative cues to be submerged by dominant low-frequency background and contextual redundancy during feature learning. Meanwhile, cross-modal spatial misalignment and spatially varying modality reliability hinder stable fine-grained correspondence, thereby degrading fusion quality. To address these issues, QA²FDet is proposed as a quality-aware adaptive alignment fusion network comprising three modules: spectrum-spatial decoupled enhancement module (SDE), cross-modal correspondence mining module (CCM), and prior-informed gated fusion (PGF). SDE leverages the discrete cosine transform to disentangle redundant low-frequency background information, while deep semantic gating propagates high signal-to-noise ratio details into shallow representations to enhance subtle cues of tiny pedestrians and suppress high-frequency noise. To establish fine-grained neighborhood correspondences under slight spatial offsets, thermal-guided local asymmetric cross-attention is designed in CCM. Finally, region-level quality and modality discrepancy are jointly modeled for adaptive cross-modal fusion in PGF. Extensive experiments on multiple UAV-based RGBT detection benchmarks demonstrate that QA²FDet achieves state-of-the-art performance and exhibits strong robustness in challenging aerial scenes.