FD-RTDETR: Frequency Enhancement and Dynamic Sequence-Feature Optimization for Object Detection
Wu Wang, Qijin Wang, Kun Zou, Yi‐Chi Huang, Qi Xu · Electronics · 2025
The high computational complexity of transformer-based detectors leads to slow inference speeds. RT-DETR demonstrates improved performance in these aspects, yet there remains room for enhancement. To achieve more comprehensive feature learning and better coverage of objects across scales, we introduce FD-RTDETR, a refined architecture for end-to-end object detection. We design a Frequency and Attention-based Intra-scale Feature Interaction module for the hybrid encoder, performing dual-path enhancement on high and low frequency features. Simultaneously, we introduce a Dynamic Fusion of Scale Sequence features module for cross-scale feature fusion, which significantly extends the model’s coverage capability across objects at different scales. Using ResNet-18 as the backbone network and evaluating on the COCO 2017 dataset, our FD-RTDETR achieves 45.1 AP, surpassing RT-DETR by 0.7 AP. On the VisDrone2019 dataset, it achieves 47.9 mAP50, outperforming RT-DETR by 1.3. Our method was also tested for generalization on urinary sediment and high-altitude infrared thermal imaging datasets, achieving 0.7 mAP50 and 0.8 mAP50:95 higher than RT-DETR, respectively, and performs better in certain categories.