الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Liang, B., Sui, C., Bai, J., Liu, Y., Li, C., Sui, X., & Chen, Q. (2026). FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection. https://omanscience.com/ar/articles/focal-frequency-oriented-cross-modal-interaction-and-spectral-calibration-for-aerial-visible-infrared-object-detection
MLA 9
Liang, Ben, et al. "FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection." https://omanscience.com/ar/articles/focal-frequency-oriented-cross-modal-interaction-and-spectral-calibration-for-aerial-visible-infrared-object-detection.
شيكاغو (المؤلف–التاريخ)
Liang, Ben, Chao Sui, Junqi Bai, Yuan Liu, Chunlai Li, Xiubao Sui, and Qian Chen. 2026. "FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection." https://omanscience.com/ar/articles/focal-frequency-oriented-cross-modal-interaction-and-spectral-calibration-for-aerial-visible-infrared-object-detection.
هارفارد
Liang, B., Sui, C., Bai, J., Liu, Y., Li, C., Sui, X. and Chen, Q. (2026) 'FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection', Available at: https://omanscience.com/ar/articles/focal-frequency-oriented-cross-modal-interaction-and-spectral-calibration-for-aerial-visible-infrared-object-detection.
فانكوفر
Liang B, Sui C, Bai J, Liu Y, Li C, Sui X, et al. FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection. https://omanscience.com/ar/articles/focal-frequency-oriented-cross-modal-interaction-and-spectral-calibration-for-aerial-visible-infrared-object-detection
IEEE
B. Liang, C. Sui, J. Bai, Y. Liu, C. Li, X. Sui, and Q. Chen, "FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection," https://omanscience.com/ar/articles/focal-frequency-oriented-cross-modal-interaction-and-spectral-calibration-for-aerial-visible-infrared-object-detection.