TSFD-Net: Two-Stage Feature Decoupling Network for Task and Parameter Discrepancies in RSOD

Abstract

Deep learning excels in natural image object detection, but remote sensing images face challenges like multidirectional objects and neighborhood interference. Existing methods use shared features for classification and regression, causing task interference. Classification needs translation/rotation-invariant features, while regression requires translation/rotation-equivariant features. Additionally, regression parameters (e.g., center, shape, and angle) demand distinct feature properties. To address this, we propose TSFD-Net, featuring: 1) task differential decoupling module (TDDM): decouples task-specific features via parallel CNN-Transformer branches, and 2) parameter differential decoupling module (PDDM): designs specialized regressors for distinct parameters (e.g., angle versus center/shape). Together, TDDM and PDDM form the two-stage feature decoupling (TSFD) structure. We further introduce dynamic cascade activation masks (DCAMs), leveraging bounding box feedback to enhance target focus and suppress neighborhood noise. TSFD network (TSFD-Net) achieves state-of-the-art results on DOTA-v1.0 (81.37% mAP), validating its efficacy.