DynaFocus-DETR: Dynamic Feature-Focused Detection Transformer for Remote Sensing Detection

Abstract

Remote sensing object detection (RSOD) faces significant challenges due to complex background clutter and the loss of fine-grained features in deep networks. To address these issues, we propose a novel dynamic feature-focused detection transformer, termed DynaFocus-DETR. First, we introduce a Dynamic Feature-Focused Transformer (DynaFocus-Transformer) module, which leverages the self-attention weights of high-level features to dynamically extract and fuse local details from lower-level features, thereby suppressing background interference and enhancing semantic alignment. Second, we design a dual-branch context-aware downsampling (DBCAD) module to reduce information loss during downsampling by fusing max-pooling features with contextually enriched features extracted via adaptive kernels. Finally, we design a Density Map-Guided Query Selection (DMGQS) method to provide high-quality queries for the decoder of the transformer. Extensive experiments on the DIOR and NWPU VHR-10 datasets demonstrate the superiority of our approach, achieving state-of-the-art mAPs of 77.9% and 94.0%, respectively. Code is available at https://github.com/Zhang-Haoyan/DynaFocus-DETR