FFR-YOLO: A Frequency-Guided Fusion Reconstruction Network for Small-Object Detection in Remote Sensing Images

Abstract

To address the challenges of small scales, weak features, complex backgrounds, and misalignment in multi-scale fusion for small-object detection in remote sensing images, this study proposes a frequency-guided fusion reconstruction YOLO (FFR-YOLO), an improved YOLOv8 framework. The method performs joint optimization across three levels: the backbone, neck, and front end of the detector head. In the backbone, a frequency-guided anti-alias progressive downsampling module utilizes Haar wavelet decomposition to replace traditional strided convolutions and incorporates a low-frequency-guided high-frequency gating mechanism to mitigate detail loss and background noise interference during downsampling. In the neck, a bridge-guided bidirectional reconstruction fusion module (BRFM) enhances the collaborative reconstruction of multi-scale semantic and detailed information via multi-source weighted fusion and cross-path bridging interactions. At the front end of the detector head, a recalibrated dual-branch local–global fusion (RDLGF) module implements dynamic allocation and complementary fusion of dual-path features. Experiments were conducted on two datasets, DIOR and NWPU VHR-10. The results demonstrate that FFR-YOLO achieves a mAP@0.5 of 85.8% and a mAP@0.5:0.95 of 63.1% on DIOR and 93.6% and 62.1% on NWPU VHR-10. These outcomes present improvements over the baseline YOLOv8, validating the effectiveness and practical value of the proposed method for small-object detection in remote sensing scenarios.