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.

The paramount value of image fusion is manifested in effectively enhancing downstream tasks. However, compatibility with subsequent tasks is compromised due to the semantic deficiency of fusion representations generated by current approaches. To mitigate this limitation, a semantic-guided multi-level collaborative fusion network is proposed, termed DSIFuse. By leveraging semantic priors and global context extracted from auxiliary segmentation branches, a multi-level interaction space is constructed to explicitly refine cross-modal features. Specifically, a cross-modal feature correction mechanism is designed to enhance semantic alignment by injecting complementary visible–infrared information at each layer, while a three-level interaction strategy gradually integrates unimodal features and semantic maps to generate semantically enriched representations. To mitigate semantic information loss during image reconstruction, a semantic compensation block is employed, incorporating interactive representations from prior layers and global semantic maps into the multi-scale decoder. Finally, the overall loss integrates semantic supervision, gradient, and intensity loss. Experiments conducted on public datasets indicate that clear fusion images are generated by DSIFuse, with improved structural consistency and reduced artifacts. Under a unified benchmark, the fused representations subsequently yield improved performance in downstream object detection tasks.