FMF‐DETR: A Frequency‐Aware Multi‐Scale Fusion DETR for Small Object Detection

ABSTRACT Small object detection, a critical technique for recognizing and localizing diminutive targets in visual data, plays a vital role in applications ranging from remote sensing and unmanned aerial vehicle (UAV) vision to autonomous driving. Current methodologies, however, face substantial challenges, including detection accuracy limitations, low‐resolution image processing difficulties, background noise interference, and target occlusion issues. To address these challenges, we propose FMF‐DETR, an innovative small object detection framework featuring frequency‐domain feature optimization through three key components: (1) High‐Low Frequency Fusion Model (HLFM), (2) Focused Diffusion Feature Pyramid Network (FDFPN), and (3) BiPathNet (BPNet). Specifically, the HLFM module enhances multiscale feature representation by emphasizing high‐frequency details while suppressing low‐frequency background noise. The FDFPN architecture improves detection performance in complex scenarios through multiscale feature fusion and saliency‐aware diffusion. BPNet introduces a dual‐path feature extraction mechanism that simultaneously enhances feature discriminability and reduces computational overhead. Through the synergistic integration of these components, the proposed framework enhances both detection accuracy and operational efficiency. Comprehensive evaluations on the VisDrone dataset demonstrate FMF‐DETR's superior performance, achieving a 2.2% accuracy improvement while reducing model parameters by 13.03M and computational complexity by 97.2G FLOPs compared to baseline methods. These results validate both the effectiveness and efficiency of our proposed framework.