MFCA-Net: Multi-Frequency Content-Adaptive Network for UAV Object Detection

Object detection performance can be improved through complementary techniques such as data augmentation, loss optimization, and image feature enhancement. Inspired by conventional image filtering, we propose a Multi-Frequency Content-Adaptive Network (MFCA-Net), which consists of Fixed-Frequency Prior Extraction (FFPE), Multi-Frequency Prior Representation (MFPR), and Content-Adaptive Frequency Fusion (CAFF). MFPR extracts three prior features using Gaussian filtering, Difference of Gaussians (DoG), and an eight-neighbor Laplacian filter. CAFF generates frequency-specific weights from the RGB and prior features, fuses them into a single-channel representation, and concatenates it with RGB as the input to the detector. We evaluate MFCA-Net with YOLOv8 and MaxViT backbones on VisDrone-DET and DroneVehicle. MFCA-Net introduces only 11,751 additional parameters while improving detection performance across different backbones and datasets. In particular, YOLOv8n+MFCA-Net improves mAP by 1.2% and mAPs by 0.9% on VisDrone-DET. These results demonstrate that the proposed frequency-based feature enhancement can improve object detection with a small increase in model complexity.