FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection

2026-09-24

AI Summary

This study addresses the insufficient cross-modal frequency interaction and improper utilization of spectral discrepancies in aerial visible-infrared object detection by proposing the FoCal framework. FoCal introduces a pioneering frequency-aware dual-domain calibration mechanism alongside discrepancy-guided spectral modulation. By integrating cross-modal attention with adaptive gating, the framework achieves low-frequency consensus fusion while preserving high-frequency specificity, thereby accurately extracting complementary information across frequency bands and avoiding indiscriminate fusion. Extensive experiments demonstrate that FoCal attains an mAP50 of 83.5% on benchmarks such as DroneVehicle. Notably, with only 3.0M parameters, it achieves an inference speed of 113.6 FPS, effectively balancing detection accuracy with computational efficiency for real-time deployment.

Abstract

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.

Problem

• Aerial object detection
• RGB-IR fusion
• Cross-modal interaction
• Spectral discrepancy
• Frequency components

Innovation

• Cross-Modal Interaction
• Spectral Calibration
• Frequency-Domain Feature Enhancement
• Aerial Object Detection
• RGB-Infrared Fusion