WE-DETR: Wavelet-Enhanced Multiscale Feature Compensation Detection Transformer for Remote Sensing Small Objects

Abstract

Small object detection in remote sensing imagery remains challenging due to the extremely limited pixel footprint of targets and the severe loss of fine-grained spatial details caused by multistage downsampling. To address these issues, we propose WE-DETR, a wavelet-enhanced multiscale feature compensation detection transformer. WE-DETR establishes a remote sensing-oriented progressive high-frequency (HF) feature compensation mechanism, which aims to preserve, enhance, fuse, and propagate discriminative structural cues throughout the detection pipeline. In particular, wavelet decomposition is introduced at the early backbone stage to preserve fine-grained details before downsampling-induced degradation occurs. A low-frequency-guided structural enhancement strategy is further developed to selectively strengthen informative HF components. The enhanced frequency-domain representations are then adaptively integrated with spatial-domain semantic features to improve detail preservation while suppressing clutter-induced responses. Finally, the compensated shallow details are propagated into multiscale detection features using detail-preserving downsampling and efficient contextual enhancement. Extensive experiments on multiple remote sensing benchmarks, including optical, infrared, and synthetic aperture radar (SAR) datasets, demonstrate that WE-DETR achieves competitive performance compared with state-of-the-art detectors while maintaining a favorable balance between accuracy and computational cost. These results validate the effectiveness of the proposed wavelet-enhanced multiscale feature compensation framework across representative remote sensing small object detection tasks. The code is available at https://github.com/jingmingliang/WE-DETR