This paper presents a novel object detector called DEYOv2, an improved
version of the first-generation DEYO (DETR with YOLO) model. DEYOv2, similar to
its predecessor, DEYOv2 employs a progressive reasoning approach to accelerate
model training and enhance performance. The study delves into the limitations
of one-to-one matching in optimization and proposes solutions to effectively
address the issue, such as Rank Feature and Greedy Matching. This approach
enables the third stage of DEYOv2 to maximize information acquisition from the
first and second stages without needing NMS, achieving end-to-end optimization.
By combining dense queries, sparse queries, one-to-many matching, and
one-to-one matching, DEYOv2 leverages the advantages of each method. It
outperforms all existing query-based end-to-end detectors under the same
settings. When using ResNet-50 as the backbone and multi-scale features on the
COCO dataset, DEYOv2 achieves 51.1 AP and 51.8 AP in 12 and 24 epochs,
respectively. Compared to the end-to-end model DINO, DEYOv2 provides
significant performance gains of 2.1 AP and 1.4 AP in the two epoch settings.
To the best of our knowledge, DEYOv2 is the first fully end-to-end object
detector that combines the respective strengths of classical detectors and
query-based detectors.

本文提出了一种名为 DEYOv2 的新型物体检测器，它利用渐进式推理方法加速模型训练并提高性能，采用一对多匹配和端到端优化的方式，结合经典检测器和查询型检测器的优点，在同等设置下超过了所有现有的基于查询的端到端检测器。