FoCal：面向航空可见光-红外目标检测的频率导向跨模态交互与频谱校准

中文摘要 AI 辅助

在航空RGB-IR目标检测中，有效利用跨模态互补信息对于在复杂光照和环境条件下的鲁棒感知至关重要。现有的多模态检测器主要关注空间域交互或特定频率特征增强，而不同频率分量的跨模态交互模式仍未得到充分探索。此外，频谱差异本身可能既包含有用的互补线索，也包含不可靠的模态特定响应，使得不加区分的频率融合效果欠佳。为解决这些问题，我们提出了FoCal，一个面向航空RGB-IR目标检测的频率导向框架。首先，开发了频率感知双域校准（FADC）模块，以显式建模频率相关的跨模态交互。低频分量被协同整合为共享的结构共识，而高频分量通过选择性跨模态交换保留模态特定信息。得到的频率感知线索进一步转移到原始特征域以调节跨模态校准。其次，我们引入了差异引导的频谱调制（DGSM）模块，该模块利用置信度加权的相对幅度差异来表征跨模态频谱不平衡，并将其转换为有界符号门，用于对联合多模态频谱进行自适应增强、保留或衰减。在DroneVehicle、ESCVehicle和ATR-UMOD上的大量实验证明了FoCal的有效性，分别取得了83.5%、54.8%和64.6%的mAP50值。同时，仅用3.0M参数，FoCal在保持领先检测精度的同时达到113.6 FPS，凸显了良好的精度-效率权衡。代码可在{此https URL}获取。

英文摘要

In aerial RGB--IR object detection, effectively exploiting complementary information across modalities is critical for robust perception under complex illumination and environmental conditions. Existing multimodal detectors mainly focus on spatial-domain interaction or frequency-specific feature enhancement, while the cross-modal interaction patterns of different frequency components remain insufficiently explored. Moreover, spectral discrepancy itself may contain both useful complementary cues and unreliable modality-specific responses, making indiscriminate frequency fusion suboptimal. To address these issues, we propose FoCal, a frequency-oriented framework for aerial RGB--IR object detection. First, a Frequency-Aware Dual-Domain Calibration (FADC) module is developed to explicitly model frequency-dependent cross-modal interaction. Low-frequency components are collaboratively consolidated into a shared structural consensus, whereas high-frequency components preserve modality-specific information through selective cross-modal exchange. The resulting frequency-aware cues are further transferred to the original feature domain to regulate cross-modal calibration. Second, we introduce a Discrepancy-Guided Spectral Modulation (DGSM) module, which characterizes cross-modal spectral imbalance using confidence-weighted relative amplitude discrepancy and transforms it into a bounded signed gate for adaptive enhancement, preservation, or attenuation of the joint multimodal spectrum. Extensive experiments on DroneVehicle, ESCVehicle, and ATR-UMOD demonstrate the effectiveness of FoCal, yielding $\mathrm{mAP}_{50}$ values of 83.5\%, 54.8\%, and 64.6\%, respectively. Meanwhile, with only 3.0M parameters, FoCal achieves 113.6 FPS while preserving leading detection accuracy, highlighting a favorable accuracy--efficiency trade-off. Code is available at {https://github.com/universeliang/FoCal.

发表机构

• Nanjing University of Science and Technology（南京理工大学）
• Shanghai Institute of Technical Physics, Chinese Academy of Sciences（中国科学院上海技术物理研究所）
• China Electronics Technology Group Corporation（中国电子科技集团公司）

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