Towards Rotated Object Detection with Pose-Aware and Dense Feature Modulation

Remote sensing image object detection is a core task in computer vision, which plays a vital role in intelligent transportation, port monitoring, and infrastructure management. However, rotated dense objects in remote sensing scenes suffer from severe challenges, including arbitrary 0–360° pose variations, large-scale differences, dense spatial aggregation, and blurred boundaries. Traditional Convolutional Neural Networks rely on fixed sampling grids and receptive fields, failing to adaptively capture the dynamic morphological and pose features of tilted targets. Meanwhile, existing methods struggle to address feature coupling and boundary misjudgment among densely arranged objects, leading to degraded detection accuracy. To tackle these bottlenecks, we propose an adaptive detection framework named PDNet for rotated dense object detection. The framework integrates four key designs: First, a Pose-Aware Dynamic Sampling Mechanism (PDSM) is developed to estimate the target principal axis in real time and learn a deformable offset field, which dynamically adjusts the convolution sampling pattern and receptive field shape to adapt to target pose variations. Second, a Dense-Scene Feature Modulation Mechanism (DSFM) constructs a dynamic weight field based on local feature responses to enhance discriminative target features and suppress inter-target interference in dense regions. Third, the Strip-Based Context Attention (SCA) module fuses global and local contextual information to strengthen the representation of small and weak targets. Fourth, a boundary-aware rotation loss function is designed to optimize the regression accuracy of rotated bounding boxes via pixel-level supervision. Extensive experiments on DOTA-v1.0, AI-TOD achieve 79.86% mAP and 25.95% AP, outperforming state-of-the-art methods.