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.