FAMA-DET: A Frequency-Domain Adaptive Multi-Scale Attention Detection Network for Aircraft Target Detection in Optical Remote Sensing Images

Aircraft target detection in optical remote sensing imagery is hindered by severe scale variation, cluttered backgrounds, and the limited capacity of the spatial-domain convolution to represent frequency-selective target features. We propose FAMA-DET, a frequency-domain adaptive detection framework built on YOLO11, which pursues a unified design principle of content-adaptive spectral representation across all architectural levels. The Frequency-Domain Adaptive Cross-Stage Feature Extractor (FDACFE) replaces static kernels with frequency-domain parameterised convolution driven by learnable DFT basis vectors, enabling differentiated perception of high-frequency edge details and low-frequency semantic components. The Soft-Aligned Bidirectional Feature Pyramid Network (SABFPN) eliminates upsampling amplitude distortion through scale-normalised interpolation and enriches cross-scale fusion with multi-receptive-field textural modelling. The Adaptive Multi-Scale Recalibrated Decoupled Detection Head (AMRDDHead) embeds multi-scale channel recalibration into both localisation and classification branches to suppress background redundancy and reinforce discriminative activations. On MAR20, FAMA-DET improves mAP50 and mAP50-95 over the YOLO11n baseline by 1.8% and 1.6% at only 5.4 GFLOPs, while maintaining real-time throughput of 109.7 FPS. Under zero-shot cross-domain transfer to CORS-ADD, FAMA-DET achieves the highest mAP50 of 93.3% among all compared methods, surpassing RT-DETR-R18 in mAP50 while using 91.0% fewer GFLOPs, confirming that frequency-domain adaptive design yields both strong generalisation and deployment efficiency.