Frequency Decoupling and Recalibration for Small Object Detection in Remote Sensing Images via Discrete Wavelet Transform

The rapid advancement of deep learning has significantly improved the performance of remote sensing object detection. However, detection performance for small objects remains unsatisfactory. In this paper, we identify frequency aliasing as a key challenge underlying small object detection performance degradation and propose DWT-DETR, a novel frequency decoupling and recalibration small object detector using discrete wavelet transform (DWT). The proposed DWT-DETR employs DWT to obtain explicit frequency-band representations and further models them according to the different requirements of feature encoding and multi-scale feature fusion. In the feature encoding stage, we design a frequency-band mixing module (DWT-FBM) to mitigate the degradation of high-frequency details of small objects in deeper layers. The DWT-FBM first employs Haar wavelets to decompose features into frequency sub-bands, then applies cross-frequency interaction and dynamic amplitude modulation to adaptively recalibrate the frequency information. Finally, the refined features are reconstructed via inverse wavelet transform. During feature fusion, we design a frequency-guided fusion module (DWT-FGF) to ensure spatial alignment and discrimination of the fused features. The DWT-FGF employs a dual-path mechanism that decouples shallow features into low-frequency and high-frequency sub-bands via wavelet decomposition. The low-frequency sub-band serves as a spatial reference to guide deep upsampling and reduce geometric deformation, while the high-frequency sub-bands are used to suppress background noise. Extensive experiments on AI-TOD, DIOR, and VisDrone datasets demonstrate the effectiveness of DWT-DETR, which achieves 30.8% AP, 79.4% mAP, and 32.6% AP, respectively.