PGWave-RotNe: A Novel Lightweight Network for Oriented Object Detection in Remote Sensing

Accurate and efficient oriented detection is critical for remote sensing images, yet remains challenging due to multi-scale distribution, arbitrary orientations, and stringent computational constraints of onboard platforms. To mitigate these challenges, we propose Partial-Ghost Shuffle Convolution and Gated Position-Sensitive Attention Wavelet Rotation Network (PGWave-RotNet), a lightweight wavelet-guided rotation detector that explicitly enhances multi-scale and arbitrarily oriented features while maintaining high efficiency. To reduce feature redundancy while preserving directional diversity, we design a Partial-Ghost Shuffle Convolution (PGSConv) module that integrates partial convolution with ghost shuffle. Next, to adaptively refine multi-scale and arbitrarily oriented contexts, we introduce a Gated Position-Sensitive Attention (GPSA) module with a learnable gating mechanism. To suppress aliasing and sharpen edges during upsampling, we propose a Directional-Biased Wavelet Transform Upsampling (DBWTU) module based on high-frequency wavelet reconstruction. Additionally, we develop a Weighted Cosine Angular Loss (WCAL) to improve orientation precision for square-like targets. Experiments on DOTAv1 and DIOR-R achieve 82.27% and 83.82% mAP50, outperforming existing methods. These innovations collectively enable efficient and accurate oriented detection in remote sensing.