Frequency-Guided Cross-Modal Feature Fusion for Lightweight Visible-Infrared Small Object Detection

Visible-infrared detection improves perception in degraded illumination by combining visible texture with thermal contrast. Small objects remain difficult because their discriminative responses are weak, spatially sparse, and sensitive to cross-modal displacement. We present FGCFF-Net, a lightweight frequency-guided fusion detector for visible-infrared small object detection. The framework casts fusion as progressive evidence selection. A lightweight channel pruning module suppresses redundant dual-stream responses; frequency-guided alignment estimates residual spectral compensation; a cross-modal alignment and small-target framework predicts bidirectional spectral weights and deformable offsets; and a frequency-aware cross-modal aggregator conditions channel-spatial attention on frequency discrepancy. The training objective further couples dense detection loss with spectral consistency, offset smoothness, and small-target emphasis. On FLIR, M3FD, LLVIP, and KAIST, FGCFF-Netachieved 92.4% mAP@0.50 on FLIR, 90.7% mAP@0.50 on M3FD, 98.5% AP@0.50 on LLVIP, and 5.72% log-average miss rate on KAIST, with 14.8M parameters and 68 FPS. Together, the results show that frequency-aware alignment can improve multispectral small-object detection without requiring a large model.