RT-DETRv3: Real-time End-to-End Object Detection with Hierarchical Dense Positive Supervision
<|reference_start|>RT-DETRv3: Real-time End-to-End Object Detection with Hierarchical Dense Positive Supervision: RT-DETR is the first real-time end-to-end transformer-based object detector. Its efficiency comes from the framework design and the Hungarian matching. However, compared to dense supervision detectors like the YOLO series, the Hungarian matching provides much sparser supervision, leading to insufficient model training and difficult to achieve optimal results. To address these issues, we proposed a hierarchical dense positive supervision method based on RT-DETR, named RT-DETRv3. Firstly, we introduce a CNN-based auxiliary branch that provides dense supervision that collaborates with the original decoder to enhance the encoder feature representation. Secondly, to address insufficient decoder training, we propose a novel learning strategy involving self-attention perturbation. This strategy diversifies label assignment for positive samples across multiple query groups, thereby enriching positive supervisions. Additionally, we introduce a shared-weight decoder branch for dense positive supervision to ensure more high-quality queries matching each ground truth. Notably, all aforementioned modules are training-only. We conduct extensive experiments to demonstrate the effectiveness of our approach on COCO val2017. RT-DETRv3 significantly outperforms existing real-time detectors, including the RT-DETR series and the YOLO series. For example, RT-DETRv3-R18 achieves 48.1% AP (+1.6%/+1.4%) compared to RT-DETR-R18/RT-DETRv2-R18 while maintaining the same latency. Meanwhile, it requires only half of epochs to attain a comparable performance. Furthermore, RT-DETRv3-R101 can attain an impressive 54.6% AP outperforming YOLOv10-X. Code will be released soon.<|reference_end|>
@article{wang2024rt-detrv3:,
    title={RT-DETRv3: Real-time End-to-End Object Detection with Hierarchical Dense
  Positive Supervision},
    author={Shuo Wang, Chunlong Xia, Feng Lv, Yifeng Shi},
    journal={arXiv preprint arXiv:2409.08475},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08475},
    primaryClass={cs.CV}
}